{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Improving sentence embeddings with BERT and metric learning.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpgmHHuNR7ly",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UQy3ffR7sbOa",
        "colab_type": "text"
      },
      "source": [
        "# Improving sentence embeddings with BERT and Representation Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loumnYbOsXVr",
        "colab_type": "text"
      },
      "source": [
        "In this experiment we fine-tune a BERT model to improve it's capability for encoding short texts. This yields more useful sentence representations for downstream NLP tasks.\n",
        "\n",
        "While a vanilla BERT can be used for encoding sentences, the representations generated with it are not robust. As we can see below, the samples deemed similar by the model are often more lexically than semantically related. Small perturbations in input samples result in large changes of predicted similarity."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BnzVpfZNshgg",
        "colab_type": "text"
      },
      "source": [
        "To improve, we use the Stanford Natural Language Inference dataset, which contains sentence pairs manually labeled with entailment, contradiction, and neutral. For these sentences we will be learning such a representation, that the similarity between the entailing pairs is greater than the similarity between the contradicting ones.\n",
        "\n",
        "To evaluate the quality of learned embeddings we measure Pearson rank correlation on STS and SICK-R datasets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FKM6rquJsh2d",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "The plan for this experiment is:\n",
        " \n",
        "\n",
        "1.   preparing the SNLI and MNLI datasets\n",
        "2.   implementing the data generator\n",
        "3.   defining the loss\n",
        "4.   building the model\n",
        "5.   preparing the evaluation pipeling\n",
        "6.   training the model\n",
        "\n",
        "\n",
        "## What is in this guide?\n",
        "This guide is about learning efficient sentence representations with BERT.\n",
        "It contains code for building and training a sentence encoder on labeled data.\n",
        "## What does it take?\n",
        "For a familiar reader it should take around 90 minutes to finish this guide and train the sentence encoder. The code was tested with tensorflow==1.15.\n",
        "## OK, show me the code. \n",
        "This time, most of the code from the [previous experiment ](https://medium.com/r/?url=https%3A%2F%2Ftowardsdatascience.com%2Ffine-tuning-bert-with-keras-and-tf-module-ed24ea91cff2)is reused. I recommend checking it out first. The standalone version can be found in the [repository](https://github.com/gaphex/bert_experimental/commits/master)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ko9HB2M4Lfsd",
        "colab_type": "text"
      },
      "source": [
        "## Step 1: Setting up"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uLJj350Lf4r",
        "colab_type": "code",
        "outputId": "d23814c4-e9a4-4cf3-9bde-910742e86e17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 97
        }
      },
      "source": [
        "import re\n",
        "import os\n",
        "import sys\n",
        "import json\n",
        "import nltk\n",
        "\n",
        "import logging\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.callbacks import Callback\n",
        "\n",
        "from scipy.stats import spearmanr, pearsonr\n",
        "from glob import glob\n",
        "\n",
        "nltk.download('punkt')\n",
        "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RayOY1m3tTlh",
        "colab_type": "text"
      },
      "source": [
        "We begin with downloading the SNLI, MNLI, STS and SICK datasets and the pre-trained english BERT model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0EXY9EHSyL6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5ff4dff8-a986-4ecf-994e-68877eb56491"
      },
      "source": [
        "!wget https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip\n",
        "!unzip uncased_L-12_H-768_A-12.zip\n",
        "!wget https://nlp.stanford.edu/projects/snli/snli_1.0.zip\n",
        "!unzip snli_1.0.zip\n",
        "!wget https://www.nyu.edu/projects/bowman/multinli/multinli_1.0.zip\n",
        "!unzip multinli_1.0.zip"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-01 18:18:09--  https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.24.128, 2404:6800:4003:c04::80\n",
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            "\n",
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            "--2020-03-01 18:18:26--  https://nlp.stanford.edu/projects/snli/snli_1.0.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
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            "--2020-03-01 18:18:52--  https://www.nyu.edu/projects/bowman/multinli/multinli_1.0.zip\n",
            "Resolving www.nyu.edu (www.nyu.edu)... 216.165.47.12, 2607:f600:1002:6113::100\n",
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            "  inflating: multinli_1.0/multinli_1.0_dev_matched.txt  \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQcmp8V3S8Uo",
        "colab_type": "code",
        "outputId": "7022e446-9501-46d4-9177-935e0a312dfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "!git clone https://github.com/gaphex/bert_experimental\n",
        "!git clone https://github.com/brmson/dataset-sts\n",
        "\n",
        "sys.path.insert(0, 'bert_experimental')\n",
        "sys.path.insert(0, 'dataset-sts/pysts')\n",
        "\n",
        "from bert_experimental.finetuning.text_preprocessing import build_preprocessor\n",
        "from bert_experimental.finetuning.bert_layer import BertLayer\n",
        "from bert_experimental.finetuning.modeling import BertConfig, BertModel, build_bert_module\n",
        "\n",
        "from loader import load_sts, load_sick2014"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'bert_experimental'...\n",
            "remote: Enumerating objects: 139, done.\u001b[K\n",
            "remote: Counting objects: 100% (139/139), done.\u001b[K\n",
            "remote: Compressing objects: 100% (103/103), done.\u001b[K\n",
            "remote: Total 139 (delta 74), reused 85 (delta 33), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (139/139), 217.82 KiB | 353.00 KiB/s, done.\n",
            "Resolving deltas: 100% (74/74), done.\n",
            "Cloning into 'dataset-sts'...\n",
            "remote: Enumerating objects: 3477, done.\u001b[K\n",
            "remote: Total 3477 (delta 0), reused 0 (delta 0), pack-reused 3477\u001b[K\n",
            "Receiving objects: 100% (3477/3477), 110.86 MiB | 13.18 MiB/s, done.\n",
            "Resolving deltas: 100% (2297/2297), done.\n",
            "Checking out files: 100% (212/212), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XaA9tKumTOL4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LOomQtQJtrNL",
        "colab_type": "text"
      },
      "source": [
        "Define a SNLI loader function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jfsr0nWEUB2-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from collections import Counter, defaultdict\n",
        "\n",
        "def load_snli(fpaths):\n",
        "    sa, sb, lb = [], [], []\n",
        "    fpaths = np.atleast_1d(fpaths)\n",
        "    for fpath in fpaths:\n",
        "        with open(fpath) as fi:\n",
        "            for line in fi:\n",
        "                sample = json.loads(line)\n",
        "                sa.append(sample['sentence1'])\n",
        "                sb.append(sample['sentence2'])\n",
        "                lb.append(sample['gold_label'])\n",
        "    return sa, sb, lb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9DcHZorUIOg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wstd9xMMtvYN",
        "colab_type": "text"
      },
      "source": [
        "To handle the dataset more conveniently, we put it in a dictionary. For each unique anchor we create an ID and an entry, containing the anchor, entailment and contradiction samples. The anchors lacking at least one sample of each class are filtered out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIXkEROiUL7W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def prepare_snli(sa, sb, lb):\n",
        "    \n",
        "    classes = {\"entailment\", \"contradiction\"}\n",
        "    anc_to_pairs = defaultdict(list)\n",
        "    filtered = {}\n",
        "    skipped = 0\n",
        "\n",
        "    for xa, xb, y in zip(sa, sb, lb):\n",
        "        anc_to_pairs[xa].append((xb, y))\n",
        "\n",
        "    anchor_id = 0\n",
        "    for anchor, payload in anc_to_pairs.items(): \n",
        "        \n",
        "        filtered[anchor_id] = defaultdict(list)\n",
        "        filtered[anchor_id][\"anchor\"].append(anchor)\n",
        "        \n",
        "        labels = set([t[1] for t in payload])\n",
        "        \n",
        "        if len(labels&classes) == len(classes):\n",
        "            for text, label in payload:\n",
        "                filtered[anchor_id][label].append(text)\n",
        "            anchor_id += 1\n",
        "        else:\n",
        "            skipped += 1\n",
        "            \n",
        "    print(\"Loaded: {} \\nSkipped: {}\".format(anchor_id, skipped))\n",
        "        \n",
        "    return filtered"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dflYLIWWt0Hc",
        "colab_type": "text"
      },
      "source": [
        "Load the SNLI and MNLI datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6PdTdY7_UMN_",
        "colab_type": "code",
        "outputId": "a2b795e3-4ffe-4385-9185-5d15422ebd7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "train_data = [\"./snli_1.0/snli_1.0_train.jsonl\", \"./multinli_1.0/multinli_1.0_train.jsonl\"]\n",
        "test_data = [\"./snli_1.0/snli_1.0_test.jsonl\", \"./multinli_1.0/multinli_1.0_dev_matched.jsonl\"]\n",
        "\n",
        "tr_a, tr_b, tr_l = load_snli(train_data)\n",
        "ts_a, ts_b, ts_l = load_snli(test_data)\n",
        "\n",
        "fd_tr = prepare_snli(tr_a, tr_b, tr_l)\n",
        "fd_ts = prepare_snli(ts_a, ts_b, ts_l)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded: 277277 \n",
            "Skipped: 1603\n",
            "Loaded: 5853 \n",
            "Skipped: 804\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gkwi7ekDt3-s",
        "colab_type": "text"
      },
      "source": [
        "# Step 2: Data Generator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7e1UptUdwGTG",
        "colab_type": "text"
      },
      "source": [
        "For training the model we will sample triplets, consisting of an anchor, a positive sample and a negative sample. To handle complex batch generation logic we use the following code:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KppWq8Sht5i9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class TripletGenerator:\n",
        "    def __init__(self, datadict, hard_frac = 0.5, batch_size=256):\n",
        "        self.datadict = datadict\n",
        "        self._anchor_idx = np.array(list(self.datadict.keys()))\n",
        "        self._hard_frac = hard_frac\n",
        "        self._generator = self.generate_batch(batch_size)\n",
        "\n",
        "    def generate_batch(self, size):\n",
        "        while True:\n",
        "\n",
        "            hards = int(size*self._hard_frac)\n",
        "            anchor_ids = np.array(np.random.choice(self._anchor_idx, size, replace=False))\n",
        "\n",
        "            anchors = self.get_anchors(anchor_ids)\n",
        "            positives = self.get_positives(anchor_ids)\n",
        "            negatives = np.hstack([self.get_hard_negatives(anchor_ids[:hards]),\n",
        "                                   self.get_random_negatives(anchor_ids[hards:])])\n",
        "            labels = np.ones((size,1))\n",
        "\n",
        "            assert len(anchors) == len(positives) == len(negatives) == len(labels) == size\n",
        "\n",
        "            yield [anchors, positives, negatives], labels\n",
        "            \n",
        "    def get_anchors(self, anchor_ids):\n",
        "        classes = ['anchor']\n",
        "        samples = self.get_samples_from_ids(anchor_ids, classes)\n",
        "        return samples\n",
        "    \n",
        "    def get_positives(self, anchor_ids):\n",
        "        classes = ['entailment']\n",
        "        samples = self.get_samples_from_ids(anchor_ids, classes)\n",
        "        return samples\n",
        "\n",
        "    def get_hard_negatives(self, anchor_ids):\n",
        "        classes = ['contradiction']\n",
        "        samples = self.get_samples_from_ids(anchor_ids, classes)\n",
        "        return samples\n",
        "\n",
        "    def get_random_negatives(self, anchor_ids):\n",
        "        samples = []\n",
        "        classes = ['contradiction', 'neutral','entailment']\n",
        "        for anchor_id in anchor_ids:\n",
        "\n",
        "            other_anchor_id = self.get_random(self._anchor_idx, anchor_id)\n",
        "            avail_classes = list(set(self.datadict[other_anchor_id].keys()) & set(classes))\n",
        "            sample_class = self.get_random(avail_classes)\n",
        "            sample = self.get_random(self.datadict[other_anchor_id][sample_class])\n",
        "            samples.append(sample)\n",
        "        samples = np.array(samples)\n",
        "        return samples\n",
        "    \n",
        "    def get_samples_from_ids(self, anchor_ids, classes):\n",
        "        samples = []\n",
        "        for anchor_id in anchor_ids:\n",
        "            sample_class = self.get_random(classes)\n",
        "            sample = self.get_random(self.datadict[anchor_id][sample_class])\n",
        "            samples.append(sample)\n",
        "        samples = np.array(samples)\n",
        "        return samples\n",
        "\n",
        "    @staticmethod\n",
        "    def get_random(seq, exc=None):\n",
        "        if len(seq) == 1:\n",
        "            return seq[0]\n",
        "                                      \n",
        "        selected = None\n",
        "        while selected is None or selected == exc:\n",
        "            selected = np.random.choice(seq)\n",
        "        return selected"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "52uHWJljwMGA",
        "colab_type": "text"
      },
      "source": [
        "The high-level logic is contained in the generate_batch method.\n",
        "\n",
        "\n",
        "1.   Batch anchor IDs are selected randomly from all available IDs.\n",
        "2.   Anchor samples are retrieved from anchor samples of their IDs.\n",
        "3.   Positive samples are retrieved from entailment samples of their IDs.\n",
        "4.   Negative samples are retrieved from contradiction samples of their IDs.       These may be considered hard negative samples, because they are often semantically similar to their anchors. To reduce overfitting we mix them with random negative samples retrieved from other, random ID."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rsDkNG6CuKZi",
        "colab_type": "text"
      },
      "source": [
        "# Step 3: loss function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9_jhi5-wmw7",
        "colab_type": "text"
      },
      "source": [
        "For the similarity function S we use inner product. The code for computing the approximate negative log probability of the data is below"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVlWT682uJWg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def entropy_loss(vectors):\n",
        "    anc, pos, neg = vectors\n",
        "\n",
        "    pos_sim = tf.reduce_sum((anc * pos), axis=-1, keepdims=True)\n",
        "    neg_mul = tf.matmul(anc, neg, transpose_b=True)\n",
        "    neg_sim = tf.log(tf.reduce_sum(tf.exp(neg_mul), axis=-1, keepdims=True))\n",
        "\n",
        "    loss = tf.nn.relu(neg_sim - pos_sim)\n",
        "\n",
        "    return loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDAKn-GFuJg4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IPvFc9Y3uUWI",
        "colab_type": "text"
      },
      "source": [
        "# Step 4: model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4npgqxEZw36S",
        "colab_type": "text"
      },
      "source": [
        "We re-use the fine-tuning code from the previous experiment and build the BERT module."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1SMC-hiNDQn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BERT_DIR = \"/content/uncased_L-12_H-768_A-12/\" #@param {type:\"string\"}\n",
        "\n",
        "build_bert_module(BERT_DIR+\"bert_config.json\",\n",
        "                  BERT_DIR+\"vocab.txt\",\n",
        "                  BERT_DIR+\"bert_model.ckpt\", \n",
        "                  \"bert_module\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5RN0k9sqw-yJ",
        "colab_type": "text"
      },
      "source": [
        "The model has three inputs for the anchor, positive and negative samples. A BERT layer with a mean pooling operation is used as a shared text encoder. Text preprocessing is handled automatically by the layer. Negative log probability loss is passed through the entropy_loss function.\n",
        "\n",
        "For convenience, we create 3 Keras models: enc_model for encoding sentences, sim_model for computing similarity between sentence pairs and trn_model for training. All models use shared weights."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4DgIbqZuW9r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def dot_product(vpacks):\n",
        "    u, v = tensor_pair\n",
        "    return tf.reduce_sum((u * v), axis=-1, keepdims=True)\n",
        "\n",
        "def cosine_similarity(tensor_pair):\n",
        "    u, v = tensor_pair\n",
        "    u = tf.math.l2_normalize(u, axis=-1)\n",
        "    v = tf.math.l2_normalize(v, axis=-1)\n",
        "    return tf.reduce_sum((u * v), axis=-1, keepdims=True)\n",
        "\n",
        "def mean_loss(y_true, y_pred):\n",
        "    mean_pred = tf.reduce_mean(y_pred - 0 * y_true)\n",
        "    return mean_pred\n",
        "\n",
        "def build_model(module_path, seq_len = 24, tune_lr=6, loss = entropy_loss):\n",
        "\n",
        "    inp_anc = tf.keras.Input(shape=(1, ), dtype=tf.string, name='input_anchor')\n",
        "    inp_pos = tf.keras.Input(shape=(1, ), dtype=tf.string, name='input_positive')\n",
        "    inp_neg = tf.keras.Input(shape=(1, ), dtype=tf.string, name='input_negative')\n",
        "\n",
        "    sent_encoder = BertLayer(module_path, seq_len, n_tune_layers=tune_lr, do_preprocessing=True,\n",
        "                             verbose=False, pooling=\"mean\", trainable=True, tune_embeddings=False)\n",
        "    \n",
        "    # magnitude multiplier to avoid NaN loss overflow\n",
        "    c = 0.5\n",
        "  \n",
        "    anc_enc = c * sent_encoder(inp_anc) \n",
        "    pos_enc = c * sent_encoder(inp_pos) \n",
        "    neg_enc = c * sent_encoder(inp_neg) \n",
        "\n",
        "    loss = tf.keras.layers.Lambda(loss, name='loss')([anc_enc, pos_enc, neg_enc])\n",
        "    sim = tf.keras.layers.Lambda(cosine_similarity, name='similarity')([anc_enc, pos_enc])\n",
        "\n",
        "    trn_model = tf.keras.models.Model(inputs=[inp_anc, inp_pos, inp_neg], outputs=[loss])\n",
        "    enc_model = tf.keras.models.Model(inputs=inp_anc, outputs=[anc_enc])\n",
        "    sim_model = tf.keras.models.Model(inputs=[inp_anc, inp_pos], outputs=[sim])\n",
        "\n",
        "    trn_model.compile(\n",
        "        optimizer=tf.keras.optimizers.Adam(learning_rate=1e-5, ),\n",
        "        loss=mean_loss,\n",
        "        metrics=[])\n",
        "\n",
        "    trn_model.summary()\n",
        "\n",
        "    mdict = {\n",
        "        \"enc_model\": enc_model,\n",
        "        \"sim_model\": sim_model,\n",
        "        \"trn_model\": trn_model\n",
        "    }\n",
        "\n",
        "    return mdict"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dAxtJfRmxGtY",
        "colab_type": "text"
      },
      "source": [
        "# Step 5: Evaluation pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lz8IOU8PxIHh",
        "colab_type": "text"
      },
      "source": [
        "Natural Language Encoders are usually evaluated by encoding labeled sentence pairs, measuring similarity between them, then computing the correlation of that similarity with human judgement.\n",
        "\n",
        "We will use STS 2012–2016 and SICK 2014 dataset for evaluating our model. For all sentence pairs in the test set we compute cosine similarity. We report Pearson rank correlation with human-annotated labels.\n",
        "\n",
        "The Callback below handles the evaluation procedure and saves the provided savemodel to savepath, every time a new best result is achieved."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjozjqTtxLiM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qHgQIxTNxU05",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class RankCorrCallback(Callback):\n",
        "\n",
        "    def __init__(self, loader, filepaths, name=None, verbose=False,\n",
        "                 sim_model=None, savemodel=None, savepath=None):\n",
        "\n",
        "        self.savemodel = savemodel\n",
        "        self.savepath = savepath\n",
        "        self.sim_model = sim_model\n",
        "        self.loader = loader\n",
        "        self.verbose = verbose\n",
        "        self.name = name\n",
        "\n",
        "        self.samples, self.labels = self.load_datasets(filepaths)\n",
        "        self.best = defaultdict(int)\n",
        "        super(RankCorrCallback, self).__init__()\n",
        "\n",
        "    def load_datasets(self, filepaths):\n",
        "        _xa, _xb, _y = [], [], [] \n",
        "        for filepath in filepaths:\n",
        "            sa, sb, lb = self.loader(filepath)\n",
        "            sa = self.join_by_whitespace(sa)\n",
        "            sb = self.join_by_whitespace(sb)\n",
        "            _xa += sa\n",
        "            _xb += sb\n",
        "            _y += list(lb)\n",
        "        return [_xa, _xb], _y\n",
        "            \n",
        "    @staticmethod\n",
        "    def join_by_whitespace(list_of_str):\n",
        "        return [\" \".join(s) for s in list_of_str]\n",
        "\n",
        "    def on_epoch_begin(self, epoch, logs=None):\n",
        "\n",
        "        pred = self.sim_model.predict(self.samples, batch_size=128, \n",
        "                                      verbose=self.verbose).reshape(-1,)\n",
        "\n",
        "        for metric, func in [(\"spearman_r\", spearmanr),(\"pearson_r\", pearsonr)]:\n",
        "          coef, _ = func(self.labels, pred)\n",
        "          coef = np.round(coef, 4)\n",
        "\n",
        "          metric_name = f\"{self.name}_{metric}\"\n",
        "          message = f\"{metric_name} = {coef}\"\n",
        "          if coef > self.best[metric_name]:\n",
        "            self.best[metric_name] = coef\n",
        "            message = \"*** New best: \" + message\n",
        "            if self.savemodel and self.savepath and metric == \"spearman_r\":\n",
        "                self.savemodel.save_weights(self.savepath)\n",
        "\n",
        "          print(message)\n",
        "\n",
        "    def on_train_end(self, logs=None):\n",
        "        self.on_epoch_begin(None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_m7bPbrxU9C",
        "colab_type": "text"
      },
      "source": [
        "# Step 6: training the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJ2ifoaRUMH1",
        "colab_type": "code",
        "outputId": "ddb4e586-37b5-4ffd-b362-394db25b6a33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "model_dict = build_model(module_path=\"bert_module\", tune_lr=4, loss=entropy_loss)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_anchor (InputLayer)       [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_positive (InputLayer)     [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_negative (InputLayer)     [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "bert_layer (BertLayer)          (None, 768)          109482240   input_anchor[0][0]               \n",
            "                                                                 input_positive[0][0]             \n",
            "                                                                 input_negative[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_mul (TensorFlowOpLa [(None, 768)]        0           bert_layer[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_mul_1 (TensorFlowOp [(None, 768)]        0           bert_layer[1][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_mul_2 (TensorFlowOp [(None, 768)]        0           bert_layer[2][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "loss (Lambda)                   (None, 1)            0           tf_op_layer_mul[0][0]            \n",
            "                                                                 tf_op_layer_mul_1[0][0]          \n",
            "                                                                 tf_op_layer_mul_2[0][0]          \n",
            "==================================================================================================\n",
            "Total params: 109,482,240\n",
            "Trainable params: 28,351,488\n",
            "Non-trainable params: 81,130,752\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AOoImltk6-i4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "HFRAC = 0.5\n",
        "BSIZE = 200\n",
        "\n",
        "enc_model = model_dict[\"enc_model\"]\n",
        "sim_model = model_dict[\"sim_model\"]\n",
        "trn_model = model_dict[\"trn_model\"]\n",
        "\n",
        "tr_gen = TripletGenerator(fd_tr, hard_frac=HFRAC, batch_size=BSIZE)\n",
        "ts_gen = TripletGenerator(fd_ts, hard_frac=HFRAC, batch_size=BSIZE)\n",
        "\n",
        "clb_sts = RankCorrCallback(load_sts, glob(\"./dataset-sts/data/sts/semeval-sts/all/*test*.tsv\"), name='STS',\n",
        "                               sim_model=sim_model, savemodel=enc_model, savepath=\"encoder_en.h5\")\n",
        "clb_sick = RankCorrCallback(load_sick2014, glob(\"./dataset-sts/data/sts/sick2014/SICK_test_annotated.txt\"), \n",
        "                                name='SICK', sim_model=sim_model)\n",
        "\n",
        "callbacks = [clb_sts, clb_sick]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wq8ipxXPe5lv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0bdf2d6b-d49b-4f56-ab5b-915443fff8b8"
      },
      "source": [
        "trn_model.fit_generator(\n",
        "    tr_gen._generator, validation_data=ts_gen._generator,\n",
        "    steps_per_epoch=256, validation_steps=32, epochs=10, callbacks=callbacks)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*** New best: STS_spearman_r = 0.5418\n",
            "*** New best: STS_pearson_r = 0.5475\n",
            "*** New best: SICK_spearman_r = 0.5799\n",
            "*** New best: SICK_pearson_r = 0.6069\n",
            "Epoch 1/10\n",
            "255/256 [============================>.] - ETA: 1s - loss: 0.6872Epoch 1/10\n",
            "256/256 [==============================] - 551s 2s/step - loss: 0.6861 - val_loss: 0.4560\n",
            "*** New best: STS_spearman_r = 0.7199\n",
            "*** New best: STS_pearson_r = 0.7398\n",
            "*** New best: SICK_spearman_r = 0.723\n",
            "*** New best: SICK_pearson_r = 0.8091\n",
            "Epoch 2/10\n",
            "255/256 [============================>.] - ETA: 1s - loss: 0.3943Epoch 1/10\n",
            "256/256 [==============================] - 540s 2s/step - loss: 0.3940 - val_loss: 0.3585\n",
            "*** New best: STS_spearman_r = 0.7336\n",
            "*** New best: STS_pearson_r = 0.749\n",
            "*** New best: SICK_spearman_r = 0.7452\n",
            "*** New best: SICK_pearson_r = 0.8196\n",
            "Epoch 3/10\n",
            "255/256 [============================>.] - ETA: 1s - loss: 0.3261Epoch 1/10\n",
            "256/256 [==============================] - 540s 2s/step - loss: 0.3260 - val_loss: 0.3232\n",
            "*** New best: STS_spearman_r = 0.7415\n",
            "*** New best: STS_pearson_r = 0.7548\n",
            "*** New best: SICK_spearman_r = 0.7538\n",
            "*** New best: SICK_pearson_r = 0.8249\n",
            "Epoch 4/10\n",
            "255/256 [============================>.] - ETA: 1s - loss: 0.2950Epoch 1/10\n",
            "256/256 [==============================] - 540s 2s/step - loss: 0.2949 - val_loss: 0.2828\n",
            "*** New best: STS_spearman_r = 0.7476\n",
            "*** New best: STS_pearson_r = 0.7599\n",
            "*** New best: SICK_spearman_r = 0.7574\n",
            "*** New best: SICK_pearson_r = 0.8253\n",
            "Epoch 5/10\n",
            "255/256 [============================>.] - ETA: 1s - loss: 0.2758Epoch 1/10\n",
            "256/256 [==============================] - 540s 2s/step - loss: 0.2757 - val_loss: 0.2945\n",
            "*** New best: STS_spearman_r = 0.749\n",
            "*** New best: STS_pearson_r = 0.76\n",
            "*** New best: SICK_spearman_r = 0.7607\n",
            "*** New best: SICK_pearson_r = 0.8283\n",
            "Epoch 6/10\n",
            "255/256 [============================>.] - ETA: 1s - loss: 0.2565Epoch 1/10\n",
            "256/256 [==============================] - 541s 2s/step - loss: 0.2564 - val_loss: 0.2822\n",
            "*** New best: STS_spearman_r = 0.7511\n",
            "*** New best: STS_pearson_r = 0.7616\n",
            "*** New best: SICK_spearman_r = 0.7635\n",
            "*** New best: SICK_pearson_r = 0.8298\n",
            "Epoch 7/10\n",
            "255/256 [============================>.] - ETA: 1s - loss: 0.2426Epoch 1/10\n",
            "256/256 [==============================] - 539s 2s/step - loss: 0.2427 - val_loss: 0.2838\n",
            "*** New best: STS_spearman_r = 0.7536\n",
            "*** New best: STS_pearson_r = 0.7642\n",
            "SICK_spearman_r = 0.7624\n",
            "*** New best: SICK_pearson_r = 0.8313\n",
            "Epoch 8/10\n",
            "255/256 [============================>.] - ETA: 1s - loss: 0.2335Epoch 1/10\n",
            "256/256 [==============================] - 540s 2s/step - loss: 0.2336 - val_loss: 0.2458\n",
            "*** New best: STS_spearman_r = 0.7537\n",
            "*** New best: STS_pearson_r = 0.7644\n",
            "*** New best: SICK_spearman_r = 0.7655\n",
            "SICK_pearson_r = 0.8313\n",
            "Epoch 9/10\n",
            "255/256 [============================>.] - ETA: 1s - loss: 0.2232Epoch 1/10\n",
            "256/256 [==============================] - 540s 2s/step - loss: 0.2230 - val_loss: 0.2651\n",
            "*** New best: STS_spearman_r = 0.7564\n",
            "*** New best: STS_pearson_r = 0.765\n",
            "*** New best: SICK_spearman_r = 0.7663\n",
            "SICK_pearson_r = 0.831\n",
            "Epoch 10/10\n",
            "255/256 [============================>.] - ETA: 1s - loss: 0.2176Epoch 1/10\n",
            "256/256 [==============================] - 541s 2s/step - loss: 0.2177 - val_loss: 0.2346\n",
            "*** New best: STS_spearman_r = 0.7571\n",
            "*** New best: STS_pearson_r = 0.7668\n",
            "*** New best: SICK_spearman_r = 0.7694\n",
            "*** New best: SICK_pearson_r = 0.8337\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f72068260b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gMzHdPSHe6TP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wxqjS0XVdhvc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9ZwOBwqUoq_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqrXgVQBUqEG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWTe5Nub0BrH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aAPx04ZO0TRE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1poViDMxkL-H",
        "colab_type": "code",
        "outputId": "1b84189c-632d-447e-8d52-17720960e259",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(trn_model)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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cvH//fk0cQ9zQ0GBpadnc3EwI4fP5FRUVhoaGbBfVRxUVFX5+fn/99Venu0lH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\nP5jRwe0sMtPUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Ocp8B1VxrFl",
        "colab_type": "text"
      },
      "source": [
        "# Step 7: manual evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uk4q5clG0T6f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0005KkInQX_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "def plot_similarity(labels, features1, features2, rotation):\n",
        "\n",
        "  corr1 = compute_scores(features1)\n",
        "  corr2 = compute_scores(features2)\n",
        "  sns.set(rc={'axes.facecolor':'white', 'figure.facecolor':'white'})\n",
        "  sns.set_context(\"poster\")\n",
        "\n",
        "  fig, (ax1,ax2) = plt.subplots(ncols=2, figsize=(16, 8))\n",
        "  fig.subplots_adjust(wspace=0.02)\n",
        "\n",
        "  sns.set(font_scale=1.0)\n",
        "  g1 = sns.heatmap(\n",
        "      corr1,\n",
        "      ax=ax1,\n",
        "      cbar=False,\n",
        "      yticklabels=labels,\n",
        "      xticklabels=labels,\n",
        "      vmin=np.min(corr1),\n",
        "      vmax=np.max(corr1),\n",
        "      cmap=\"Blues\")\n",
        "  \n",
        "  g2 = sns.heatmap(\n",
        "      corr2,\n",
        "      ax=ax2,\n",
        "      cbar=False,\n",
        "      xticklabels=labels,\n",
        "      vmin=np.min(corr2),\n",
        "      vmax=np.max(corr2),\n",
        "      cmap=\"Blues\")\n",
        "  g2.set(yticks=[])\n",
        "  fig.colorbar(ax2.collections[0], ax=ax1,location=\"right\", use_gridspec=False, pad=0.01)\n",
        "  fig.colorbar(ax2.collections[0], ax=ax2,location=\"right\", use_gridspec=False, pad=0.01)\n",
        "\n",
        "  g1.set_title(\"Base BERT\")\n",
        "  g2.set_title(\"Trained model\")\n",
        "\n",
        "def compute_scores(vectors):\n",
        "  corr = np.inner(vectors, vectors)\n",
        "  cmax = np.max(corr)\n",
        "  corr /= cmax\n",
        "  return corr\n",
        "\n",
        "def run_and_plot(messages_, encoder1, encoder2):\n",
        "  message_embeddings_1 = encoder1.predict(np.atleast_1d(messages_))\n",
        "  message_embeddings_2 = encoder2.predict(np.atleast_1d(messages_))\n",
        "  plot_similarity(messages_, message_embeddings_1, message_embeddings_2, 90)\n",
        "\n",
        "def get_sents(dev_set):\n",
        "  dev_keys = list(dev_set.keys())\n",
        "  sents = []\n",
        "  for k in np.random.choice(dev_keys, 3):\n",
        "    for c in classes:\n",
        "      sents.append(dev_set[k][c][0])\n",
        "  return sents"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wzHxl7e3oypE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classes = ['anchor', 'entailment', 'contradiction']\n",
        "max_len = 30\n",
        "dev_set = {k: v for k, v in fd_ts.items() if all([len(v[c][0]) < max_len for c in classes])}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OrYgSUgyoyxo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yC3wVo_Ooy6f",
        "colab_type": "code",
        "outputId": "fba0204e-f96c-4fe7-adc9-ed950ea259ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "enc_model.load_weights(\"encoder_en.h5\")\n",
        "\n",
        "model_base_enc = build_model(module_path=\"bert_module\", tune_lr=4, loss=entropy_loss)\n",
        "base_enc_model = model_base_enc[\"enc_model\"]"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_anchor (InputLayer)       [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_positive (InputLayer)     [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_negative (InputLayer)     [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "bert_layer_1 (BertLayer)        (None, 768)          109482240   input_anchor[0][0]               \n",
            "                                                                 input_positive[0][0]             \n",
            "                                                                 input_negative[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_mul_3 (TensorFlowOp [(None, 768)]        0           bert_layer_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_mul_4 (TensorFlowOp [(None, 768)]        0           bert_layer_1[1][0]               \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_mul_5 (TensorFlowOp [(None, 768)]        0           bert_layer_1[2][0]               \n",
            "__________________________________________________________________________________________________\n",
            "loss (Lambda)                   (None, 1)            0           tf_op_layer_mul_3[0][0]          \n",
            "                                                                 tf_op_layer_mul_4[0][0]          \n",
            "                                                                 tf_op_layer_mul_5[0][0]          \n",
            "==================================================================================================\n",
            "Total params: 109,482,240\n",
            "Trainable params: 28,351,488\n",
            "Non-trainable params: 81,130,752\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFNIR_q8sNH7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IApG8jPdnoH7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xweIlOd2sNCb",
        "colab_type": "code",
        "outputId": "bd220c62-cc21-4ef0-b896-585e05668de2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 775
        }
      },
      "source": [
        "sents = get_sents(dev_set)\n",
        "run_and_plot(sents, base_enc_model, enc_model)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FJyvz8fFRYmKivv32W5Pnv/zyS92+fVuNGjW6z5EBAADc\nP7lP8rHkCwCAksRecyGFJyvr1auXJGnDhg0mz2/YsEGOjo7q3r37/QwLAAAAAADgL6PwZGWNGzdW\nvXr1FBkZqZSUlDznfv31Vx05ckSBgYHy8PAodJzr169r7ty56tq1q3x9fdW0aVP17NlTn3zyiTIz\nM/O1nzRpkry9vbV+/XqdP39e48ePV6tWreTj46OQkBAtWrRI2dnZ9/Refj/miRMn9NJLL6lFixZq\n3LixevXqpXXr1t3TeJmZmdq4caPGjRunDh06yNfXV02aNFGnTp309ttv68aNG3nap6WlqXnz5mrY\nsKESEhIKHLdXr17y9vbW119/fU/xSNK3336rgQMHqnnz5mrSpIn69OmjyMjIAtsnJSXp7bffVkhI\niBo3bqxmzZqpT58+WrFihe7cuVNgv5iYGP373//W3//+dzVu3Fj+/v7q2rWrZs+erbi4OLNiTUhI\nULdu3eTt7a1p06blW8559OhRjR07Vm3atJGPj49atmyp4cOH6/Dhw3naxcbGqkGDBgoICNDt27dN\nXiszM1OBgYHy9vbWmTNnzIoPAIDfczAYLP4CAKAksddcSOHJBvTq1Uvp6enasmVLnuO5s6ByZ0UV\n5NSpU+rWrZsWLlyolJQUBQQEyN/fX5cuXdKbb76poUOHKiMjw2TfEydOqFevXjp69KhatGihZs2a\n6eLFi5ozZ45ef/31P/V+jh49qr59++rMmTN64okn5Ovrq5MnT+rVV1/VrFmzzB7n2rVrmjhxor75\n5htVqlRJbdu2lb+/v5KSkrR48WI988wzSkpKMrZ3c3NTr169lJWVpdWrV5sc88cff9Qvv/yimjVr\nqnXr1vf0vtauXat//OMfunnzptq2bauHH35YR48e1ciRI7V9+/Z87c+fP69evXpp8eLFSk1N1ZNP\nPil/f3+dPn1aM2bMKPD3ZePGjerevbv++9//Kicnx9gvJydHS5Ys0cGDB4uM9dSpU+rbt69Onz6t\ncePGKSwsTI6OjsbzS5YsUd++fbVt2za5u7srKChItWvX1tdff63Q0FD997//NbatUaOGnnzySSUn\nJ+f7M5pr586dunLligICAvToo4+a83ECAAAAAB4ATtYOAFL37t31n//8Rxs2bNBzzz0nScrKytLG\njRtVsWJFBQUF6auvvjLZ9/bt23rppZeUmJio8ePHa8iQIXJyuvvbeuPGDY0dO1b79+/Xhx9+qNGj\nR+fr/+mnn2rUqFEaOXKkHBzu1iG///57DRgwQCtXrtQLL7wgLy+ve3o/n3/+uUJDQzV58mRjsePo\n0aMaPHiwli9frtatW6tt27ZFjlO2bFl98MEHat26tZydnfO857CwMK1fv17z5s1TWFiY8Vz//v21\nfPlyrVmzRi+99FKefpK0cuVKSdKzzz5rfL/mWrx4sRYtWqQ2bdoYj73//vuaN2+e/vOf/ygkJCRP\n+/Hjxys+Pl4hISF66623VKpUKUlSfHy8Bg0apP3792v+/PkaP368sc+xY8f02muvKScnR7NmzdIz\nzzyTZ0O4mJiYIuPcv3+/Ro8erfT0dL311lvq1q1bnvNff/21Zs+eLU9PTy1YsEBNmjQxnouOjtaw\nYcM0Y8YM+fv7q27dupKk0NBQRUZGatWqVXrmmWfyXTP3c+3fv3+R8QEAYIql96KwkZu8AACYzV5z\nITOebICHh4dat26tY8eOGQsL33zzjRITE9WlSxe5uLgU2Hf9+vWKjY1Vx44dNWzYMGPRSZIqVqyo\n8PBwOTs7a8WKFcrJycnXv1GjRho1alSeIoy/v78CAwOVnZ1t1uyaP6patapeeeWVPDNsmjRpokGD\nBkmSPvnkE7PGKVu2rIKCgvIVj0qXLq2pU6fKyclJO3fuzHOuTp06at26ta5cuZLvaYBJSUnatm2b\nSpUqpaeffvqe31doaGieopMkvfDCCypXrpzOnz+vS5cuGY8fPnxYP/30k9zc3BQWFmYsOkmSl5eX\nXnvtNUnSihUrlJ6ebjy3cOFC3blzR0OGDFHv3r3zPYWgXr16qlevXoExbty4UcOGDZPBYNDixYvz\nFZ0kacGCBZKkWbNm5Sk6SVLz5s310ksvKTMzM8+ssccff1yPPPKIfv75Zx07dixPn1OnTunw4cPy\n9PRUcHBwgbEBAFCYu1+2LfkkH2u/IwAA7o295kIKTzaiZ8+eku4WkqT/v8wu93hB9u7dK0n5Ztvk\nqlq1qmrXrq3r16/r3Llz+c63bdvW5CMWH374YUlSYmKieW/gdzp06GCyWJa7QXp0dHSh+xv90fHj\nx/Xxxx9rxowZmjx5siZNmqSwsDA5OzsrKSlJycnJedo///zzkqRVq1blOb527VplZGSoc+fOqlix\n4r2+LbVr1y7fMRcXF9WsWVNS3s/q0KFDkqQnn3zS5LXatGkjDw8PpaWl6eeff5Z0d5bb/v37JUm9\ne/e+5/jef/99TZw4Ue7u7lq5cqVatmyZr01SUpKOHTumsmXLKjAw0OQ4/v7+ku4uS/y93NlMubOb\ncuX+3Ldv3zyFTwAAUKx3DwAAIABJREFUAAAA+F+ijQgKClLFihW1adMm/eMf/1BkZKQee+wx+fj4\nFNrv4sWLkv4fe3ceX9O1/nH8ezIhAzElYiaaGKIaNdU8tdRQhEsNVb2KaulAW3Siqr+rk1bRVltq\nDDWnUkMVDVGSIhRRtGJsJDFLgkx+f7jJleZETjixc47P2+u8Knuvvc6zj8qKZ6/1LOmll17K8z3O\nnz+ftXQqU27L6Nzd3SUp22wcS1WsWNHscR8fHzk4OOj69eu6ePGiypQpc9t+kpKS9Oqrr+a6zDBT\nYmKiSpQokfV1y5YtVbVqVUVGRurPP/9UjRo1lJGRocWLF0tS1nLG/CpfvrzZ4+Y+q7i4OEm5fxaS\nVKlSJSUkJGS1vXDhgq5evSonJydVqVIlX7Ht3r1bkZGRKlq0qBYuXKgKFSqYbXfq1ClJNz+z2rVr\n37bPW+tnSVL37t01ZcoUrVmzRmPHjpWnp6cSExP1ww8/yNnZWb17985XzAAA3MpkkhzscHkBAACW\nstexkMRTIeHi4qKuXbtq/vz5euONN5SSkmLRcrDMncpat26tkiVL3ratuZk3+a1zdC9NmTJFmzZt\nUo0aNTR69GgFBASoZMmSWUvvmjdvroSEhBxLCE0mk/r376/3339fwcHBeueddxQWFqbTp0+rbt26\nqlu37h3FY25mmDWvuZP+M9WoUUOOjo46cOCA3n//fX322WdmZ51l7lTo4eGR57K4f/7/5Orqqp49\ne2rOnDlavny5Bg8erFWrVik5OVkdO3aUl5fXHccPAAAAALBPJJ4KkR49emj+/PnavHmznJyc1LVr\n1zyv8fHxUUxMjPr27Wt2KZgRTp8+bfZ4bGysMjIyVKRIEYuWumXuFPfpp5/Kz88v27nk5GSdPXs2\n12uDgoL06aefKiQkRKNHj77nxa+9vb0l/W9GmjmZ5zLbenp6qlixYrp69apOnDihypUrW/x+xYsX\n1xdffKEhQ4Zo48aNGj58uGbMmKGiRYtma5c5w83JyUmTJ0/O1z1JNz+/efPmafHixXrmmWeyljNS\nVBwAcLcy61FYsz8AAGyJvY6FhXe6y32oTp06ql+/vjw9PdWxY0eVLl06z2syi11nJmkKg/Xr1ysl\nJSXH8dWrV0uS6tevb1EtoMzaTeaWA4aGhpotlp7J3d1dPXr0UGJiombMmKHw8HB5enqqU6dOlt7G\nXWnUqJEkafPmzTlqUEnS1q1blZCQIFdX16zllI6OjnrkkUckSUuXLs33e3p4eGjWrFlq3LixwsPD\nNWTIECUlJWVr4+3tLT8/P124cOGOCsdXrlxZLVu21IkTJzRlyhT9+eefeuCBB7LuFwAAAACAW5F4\nKmQWLVqkiIgIffLJJxa17927t3x8fLRy5UpNmzZNV69ezdHm5MmTCgkJsXaouTpz5ow++eSTrGVd\nkvT777/ru+++kyQNHDjQon4y61H9s5j1vn37LPp8+vfvL5PJpFmzZikjI0M9e/bMtrtcQWrQoIHq\n1q2rpKQkTZw4MVsiLi4uTv/3f/8n6WYh9FtjGj58uBwdHTV79uysQvO3+uuvv7J2PjTHzc1NX3/9\ntVq2bKnIyEgNHjxYV65cydYmsx7Ya6+9pvDw8Bx9pKena/v27TmKi2fKLN7+zTffSJL69u2bazyT\nJk1Sx44d9cUXX+TaBgAASTLpf9tIW+Vl9A0BAJBP9joWstTOxrm5uWnmzJl67rnnNH36dC1YsEB+\nfn7y8vJSUlKSjh49quPHj6tevXpZu8oVtCeffFLBwcHavHmzAgICdP78ef32229KS0tTv3791LZt\nW4v6eeGFF/TSSy9lFbT29fVVfHy8du3apU6dOikqKirXZX2S5Ovrq2bNmik8PFwODg63TZAUhE8+\n+UQDBw5UaGioIiIi1KBBA127dk0RERFKTk7WI488opEjR2a75sEHH9TEiRM1fvx4jRs3Tl9++aVq\n166t1NRUnThxQkeOHNF//vMf+fr65vq+RYsW1YwZMzRq1Cht2LBBTz/9tGbNmpVVs6l9+/YaO3as\nPvroIw0ePFhVq1ZVtWrV5ObmpoSEBB08eFCXL1/WhAkT9NBDD+Xov3nz5qpWrZpiYmLk5uZ22/+v\n4uPjFRMTc9tlkQAASJLpv7+s2R8AALbEXsdCZjzZAX9/f/3www8aNWqUqlSpoujoaK1fv17R0dEq\nWbKknn/+eU2cOPGexVOvXj0tXrxY1atXV3h4uHbv3i0/Pz9NmjRJ77zzjsX9dOzYUfPmzVPjxo11\n5swZbd68WYmJiXrjjTf00UcfWdRH06ZNJd1ckli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MGaJS\npUpliyUlJUVr1qxRWFiYoqOjFR8fr4yMDFWsWFFt27bVs88+qxIlSuS4hzNnzuirr77Stm3bdObM\nGTk6Opdk1CoAACAASURBVMrT01M1atRQhw4d9K9//SvHNUeOHNHs2bMVERGh+Ph4FStWTAEBAXr6\n6afVunVriz672NhYXbt2TSaTyez/OwAAZLLXLaQBALCUvY6FNpV4qly5snx8fBQbG6udO3dm+8dv\nZGSkHBwc1KBBAzk5Oenrr79WREREtsRT5hI9c7OdXn75Zbm6uqpGjRpq2rSprl+/roMHD2rhwoVa\nu3atvv/+e1WuXDmrfVJSkvr06aOYmBiVKVNGzZo1U7FixRQfH68jR45o//79FieezKlatap69Oih\ndevW6erVq+rYsaOKFSuWdf7W39/O8uXLNX78eKWmpqpy5cpq06aNUlJSdOLECc2aNUv+/v7q1q3b\nHX8OtwoLC9Ps2bPl6+ur5s2b6++//1ZUVJTGjRunK1eu6Omnn87WPi4uTn379tXp06dVunTprNjm\nzp2ryMjIHDPWLPH1119rypQpkqQ6deqofv36OnPmjDZv3qxffvlFkyZNUs+ePS3ub9u2bXruueeU\nkpIiX19f1axZUwkJCXrzzTd15MiRPK+fMGGCli5dqvr166tNmzaKiYmR6b9/+y9fvqyhQ4cqKipK\nxYsXV+3ateXh4aHo6GjNnj1b69ev14IFC1S+fPms/uLj4zVmzBiVKFFC1atXV61atXTlyhXt379f\nX3/9tdavX68lS5bI09Mz65q4uDj17NlTZ8/+P3v3Hpfz/f4B/HV3lpRjDhGJDoSQCEMKOYTwdSqb\nOcxs2MbMYQxjjjOzzMwpZiFnMkTmHJ2RDjqQyBRKSee7+/dHv/te9+7KXapP3V7Px2OPB5/Tfd2f\nsutzX/f7fb1fwMjICH369IGmpiaSkpIQGhqKZ8+eKRSeTp06hcWLFyMvLw9mZmbo378/Xr58icDA\nQPj5+WH27NmYNWtWqe89MzMTc+fOhVgshouLCwwNDZW+70RE9P5R1ZV8iIiIlKWqubBGFZ6AwhFN\np06dQkBAgKzwJBaLERISAnNzc+jr66NLly5QV1dHYGAgPvnkE9m5gYGBAAqnBf3Xpk2b0L9/f+jo\n6Mi25efnY/Pmzdi+fTtWr16Nbdu2yfadPXsWDx8+RP/+/eHu7g4NDQ2586Sv9S7vs1u3bvDz80NW\nVhYWLVqk9Agnqdu3b2Pp0qUQiURYs2aNwqij2NhYqKnJz7Ys630oaseOHVi7di1Gjhwp23bs2DEs\nWrQIW7Zswfjx46GtrS3bt3z5ciQmJqJ3795wd3eHrq4ugMJRMh999BEePXpUpvd76dIlbNy4EU2a\nNIG7uzs6duwo2xcUFITp06dj+fLl6NatW4nFs6LevHmDb775Brm5uZgzZw4+//xzheuVRiwW4+zZ\nszh06BCsrKwU9i9ZsgShoaEYMmQIVqxYAX19fQCF93vjxo3YvXs3Fi9ejD179sjOMTAwwLZt29C7\nd29oamrKtmdlZWHZsmU4efIk3N3dsXTpUtm+gwcP4sWLF3B1dZX9Pkjl5OTg7t27cnFFRERg8eLF\n0NLSkr2WVHR0NKZNm4YtW7agR48esLGxKfa9Z2RkYNq0aQgNDYWtrS2WL19e6r0iIiIiIiIi1VSj\nejwBxfd5Cg8PR0ZGhuxDsJ6eHiwtLWVT64DCD8LS6VDFjXgaMmSIXLEFADQ0NDB37lw0bNgQV69e\nRVZWlmzfy5cvAQC9evWSKzpJz7Ozs3vXt/rOfvvtN4jFYkybNq3YqW5t2rRB69at5baV9T4UNXjw\nYLmiEwCMGjUKrVq1Qnp6OsLDw2XbHz9+jEuXLkFDQwMrVqyQFZ0AoGnTppg/f36Z36+7uzsAYPXq\n1XJFJwCwsbHBzJkzkZubCy8vL6Wud/bsWbx48QKmpqb47LPPFK43bty4t15j+vTpxRad7t+/Dx8f\nH7Ro0QJr166VFZ2Awvv99ddfo02bNrh58ybi4uJk++rUqQN7e3u5ohNQOAJu2bJlUFNTg4+Pj9w+\n6e/qBx98IFd0AgBtbW1Z036p3377DXl5eVi4cKFc0QkAzMzMsGDBAkgkEvz555/FvueCggLMnDkT\noaGh6N69O7Zv367wO0VERKRARZeQJiIiUpqK5sIaN+JJWniKiIiQ9XmSFqGKFpS6deuGe/fuITw8\nHB07dkRwcDDy8/PRpEkTtGzZsthrP3jwANevX0dCQgLevHkDiUQCoPCDtFgsRkJCAszNzQFAVtj4\n/fffoa+vj379+skVD4SWl5eHmzdvAkCx/XtKU5b7UFRJfX9MTEwQHx+P5ORk2bbAwEBIJBJ06dIF\nzZs3VzjH0dERurq6yMzMVCrm58+fIzw8HAYGBiUW/aQFltu3byt1TemotSFDhigUbABg2LBh8PDw\nKPUaAwcOLHb7lStXAAD29vZyo8Ck1NXV0bVrV8TGxiI0NBSmpqZy+8PDw3Hz5k08ffoUWVlZsp+R\nlpYWnj9/LtcDrUOHDvDy8sL69eshFotl00KLIxaLcf36dYhEohJjf9t9PHPmDAICAmBiYoLff/9d\n6WmhREREREREpHpqXOGpRYsWaNasGZ4+fYrg4GD07dsXgYGBEIlEctN+unXrBg8PD/j7+6Njx46y\nIkJxo53y8vKwfPlyHDlypNTXzsjIkP3Zzs4OH3/8Mfbu3Yv58+dDTU0NJiYmsLGxwcCBAxVGilS1\nlJQU5OTkQEtLq9jCTnHKcx+KKtqLqChpASQnJ0e27dmzZwBQYmwikQhGRkZK9VECIGvunpaWBktL\ny1KPTUlJUeqaSUlJAKCwUptUSe9XSiQSyTVuL0oa7x9//IE//vij1OsUjTcjIwPz5s3D5cuXSz3n\nzZs3svs+atQo+Pn54cyZM/j888+hoaGBtm3bwsbGBsOGDYO1tbXsvJcvX8qKfdIirzJxFXXv3j0A\nhcVDFp2IiEhZFb3oc3VZQpqIiEhZqpoLa1zhCSgsKp08eRL+/v7o3bs3goOD0aZNG7kVwGxsbCAS\niRAQEIDp06fLRkUV92Haw8MDR44cQZMmTbBgwQJ07twZDRo0gJaWFoDCJeHDwsJko0qkFi5ciIkT\nJ+Lvv/9GcHAwQkJC4OXlBS8vL/Tp0wfbtm2Durp6Jd6JilXe+yBV3KigqiKdUqmvrw8HB4dSj23Q\noEGZrl3S+/pvf6zi9kvv3X9J47WyskLbtm1LvU6bNm1kf96wYQMuX74MMzMzzJ07F+3bt0e9evVk\nU+/s7OyQkpIi9zNSV1fHpk2b8Omnn+Ly5csICQlBaGgo9u3bh3379mHcuHH4/vvvARSOagMKp/s5\nOzu/9f0VR1q4Kjp9koiIiIiIiN5PNbLw1L17d5w8eRIBAQGIjIzE69evMWzYMLljDAwMYGZmhuDg\nYLx+/VrWX6i4wtO5c+cAACtXrkSfPn0U9ickJJQYi7GxMSZPnozJkydDIpEgKCgIc+fOxdWrV3H8\n+HGMGTPmXd5qudWvXx/a2trIyclBYmJiiaN2inqX+1BWjRs3BvDvyJ//kkgkSExMVPp60pFF2tra\nWLt27bsHCMhWYSspjrLE91/SeHv27Il58+YpfZ70Z/Tzzz8rTL/LyMgodTSXubm5bIqkWCzG5cuX\n8fXXX8PLywuDBw+GnZ0d6tevDy0tLdnot/L0ZurcuTNyc3NhYWFR5nOJiOg9VsFLSFeTL3mJiIiU\np6K5sMY1Fwf+nS4XERGBS5cuyW0rqlu3bnjz5g3++OMP5Ofno1mzZmjRooXCcWlpaQBQ7LSoK1eu\nyPa/jUgkQrdu3WQNtqOiopR7Q6WQjmTJz88v83nS1fsOHz6s1DkVdR+UIe0TFBISUmwB5+LFi0r3\ndwIKp8OZmpri+fPnCAoKqtAYz549W+wor9OnT5f72tLC3oULF2Sjn5SRnp4OoPif0alTp5S+jrq6\nOhwcHGBvbw+gsNk5UNgjqkePHpBIJApNypXl4uKCtWvXon///uU6n4iI3k8q2k+ViIhIaaqaC2tk\n4alFixYwMjKCWCzGvn37AEBhZa6i2/bu3Qug+OIUANnKbgcOHJArMMTHx2PFihXFnuPj44OgoCCF\ngkRWVhZu3boFoOTeQGUhHRlUdGUzZc2cORPq6urYuXMnTpw4obA/Li4ODx48kP29PPehvIyNjdGv\nXz/k5+dj+fLlcivlPXv2DBs2bCjzNb/44gsAwNdffw0/Pz+F/WKxGDdv3sTdu3eVut7gwYNRv359\nxMTE4Pfff5fbFxISgoMHD5Y5RqmOHTvC3t4eDx8+xFdffSXrJ1XUq1evsH//ftn0N6CwUTsAeHp6\nyh179+5d/Pzzz8W+1rFjxxAZGamwPSUlRdYgvGi/qlmzZkFDQwOrVq0qtugmkUhw+/btYu8xAKxf\nvx5OTk44cOBAsfuJiIiIiIjo/VEjp9oBhUWlxMREpKWloVWrVmjUqFGxxwD/juQpqVnyjBkzcPPm\nTXh6esLPzw+WlpZ49eoVAgMD0bVrVzRs2BB37tyRO8ff3x+enp6oX78+2rVrh3r16iEjIwMhISFI\nS0tDmzZtyryaXHEGDBiA4OBgzJ07F7169UKdOnUAAN988w0MDAxKPbdz585Yvnw5VqxYgQULFmDr\n1q1o164dcnNzkZCQgJiYGKxfv15WcCrPfXgXK1aswIQJE3D16lU4ODigW7duyMnJgb+/P8zNzaGv\nr690kQgABg0ahPnz52Pjxo34+OOPYWJiglatWqF27dpITk5GVFQU0tPTsWrVKtmqhKXR09PDunXr\n8Nlnn2HTpk3w9vaGhYUFkpOTERwcjEmTJmHPnj2yUWlltX79esycORM+Pj64fPkyLC0tYWRkhPz8\nfCQkJCA6OhpisRhjx46V9VOaNWsWvvrqK/z44484c+YMTExMkJSUhJCQEAwfPhw3b95UKGL5+Phg\n0aJFaNKkCSwsLFCnTh2kpqYiODgYWVlZsLW1lRud1KlTJ6xZswZLlizBl19+KRtNpq+vj9TUVERF\nReHly5f49NNP0bNnT4X3lZycjIcPHyI1NbVc94WIiN5j1eWrWSIiIqGoYC6skSOeAPkiUnGjnYDC\nJtLSogpQ8ognGxsbHDp0CH379sXr16/x999/Izk5GZ999hl27NhRbIPw0aNHY9q0aWjZsiWio6Nx\n7tw5hIWFoVWrVli8eDEOHTokW1XsXXz44YeYPXs2GjVqhEuXLuHIkSM4cuSI3Aih0owdOxZHjx7F\nyJEjkZubC19fXwQHB0NdXR3Tp0+Xu4/luQ/vokmTJjh8+DDGjx8PNTU1XLx4ETExMXB1dYWHhwc0\nNMpeF502bRqOHj2KUaNGIT8/H35+frL30a1bN6xatQqDBg1S+np9+vTBwYMHYW9vj6SkJPj6+iIj\nIwMrVqzApEmTAAD16tUrc5xAYSP0P/74A2vXroWNjQ0ePXokG0kHAOPHj8fu3bvl7sOQIUOwZ88e\n2Nra4unTp7h06RKysrKwZMmSEntbTZ06FZMmTULDhg1x7949nDt3DtHR0bCyssLq1auxa9cuhXs9\nfPhweHt7w83NDdra2ggICMDFixeRkJAAS0tLLFmyBK6uruV630RERERERPT+EElKWqKMiEp19OhR\nLF68GI6Ojvj111+FDue9EPQwXegQqAoJuFCm0kwb1xY6hLdSrwE3cv3lsk8nF8Lx6/FCh/BWLr1b\nCR2CUlY6Ka7o+vB5NvILKu6xVENNBJNGZV8ko7q7+zhD6BBUQk6+8j02hWLRrI7QIShF4y0rPVcH\nCS+V7x0rlP4rytdftSql3LoodAhKsRg5SugQ3ip0WfH9cFU1F9bYqXZEVeHFixfIzc2V64EEFPZ4\n+vHHHwEUNtMmIiKidyOq4JV8akDNlYiISI6q5kIWnohKERERgenTp8PMzAzNmzeHpqYmEhISZM26\nR40aBUdHR4GjJCIiIiIiIqqeWHgiKkXbtm0xfvx4BAYGIigoCJmZmdDT04OdnR1GjRqF4cOHCx0i\nERGRSqjoZZ+ryZe8RERESlPVXMjCE1EpmjZtihUrVggdBhEREREREVGNxMITEREREQlPVb/mJSIi\nUpaK5kIWnoiIiIhIcCKIKvhZu5o8bRMRESlJVXNh9V/7koiIiIiIiIiIaiSOeCIiIiIiwanqEtJE\nRETKUtVcyBFPRERERERERERUKTjiiYiIiIgEp6L9VImIiJSmqrmQI56IiIiIiIiIiKhScMQTERER\nEQlPVb/mJSIiUpaK5kIWnoiIiIioWqguyz4TEREJRRVzIafaERERERERERFRpeCIJyKqMRrU0RI6\nBJVQU75DycjOFzqEt5JIhI7g7TQ1q/93TD2aGwgdgnJ6txI6greKevZa6BDKTVWXkK5ozIUVIztX\nLHQIb6WhVv3//w3UjH9rBrU0hQ7hrexsWwodwlvFNRkldAhKSXiQJHQI5aaqubBm/N+MiIiIiIiI\niIhqHI54IiIiIiLBqWg/VSIiIqWpai5k4YmIiIiIBKeq0wuIiIiUpaq5kFPtiIiIiIiIiIioUnDE\nExERERFVA9Xka1kiIiLBqGYu5IgnIiIiIiIiIiKqFBzxRERERESCU9W+FkRERMpS1VzIwhMRERER\nCU5VV/IhIiJSlqrmQk61IyIiIiIiIiKiSsERT0REREQkOBEqeHpBxV2KiIioSqhqLuSIJyIiIiIi\nIiIiqhQc8UREREREghNBpJJ9LYiIiJSlqrmQhSciIiIiEl5FPx1Xl6dtIiIiZaloLuRUOyIiIiIi\nIiIiqhQc8URERERE1UI1+WKWiIhIMKqYCzniiYiIiIiIiIiIKoVShaf+/fvD3Nwc/v7+lR0PUYn8\n/f1hbm6OSZMmyW1/8uQJzM3N0b9/f4EiIyIionclElX8f0RERDWJqubCdxrx5O7uDnNzc7i7u1dU\nPPQWx44dg7m5ORYuXCh0KEREREREREREpWKPJyIiIiISnKouIU1ERKQsVc2FLDwRERERkfBUdAlp\nIiIipaloLix34cnc3Fz25y1btmDLli2yv8+aNQuzZ88u8VyxWIwePXogMzMT/v7+0NPTk+27ePEi\nPvvsMwDA9u3b0bdvX9m+jIwMdO/eHbq6uvD394eaWuFMwTt37uDcuXPw9/fHs2fPkJ6ejrp166JL\nly6YMmUKrK2ti43h0KFDOHnyJGJiYpCTkwN9fX00btwY3bt3xyeffIL69euX6V7cv38fZ86cwd69\nexEdHQ0A6NixI2bPng0bG5tiz01MTMSOHTtw7do1JCUloVatWrCwsMDYsWPh7Owsd2z//v2RmJgI\nADh+/DiOHz8u2+fi4oK1a9cqFW9eXh6OHTuG06dP4/79+8jMzETDhg1hbm6OoUOHYvjw4XLxnT59\nGtevX0dCQgJevnwJXV1dmJubFxsjUNiL6cMPP4StrS12796NnTt34uTJk0hMTISenh569eqFuXPn\nolmzZsXG5+vri507d+L+/fvQ0NCAlZUVZs6cqdR7K05mZiY8PT1x7tw5PHz4EPn5+WjRogWcnJww\nZcoU1K5du0zXy8vLg4eHB44fP44nT55AX18fvXr1wpdffomjR49iy5YtCv8G3N3dZdtHjRqFLVu2\n4MaNG3jx4gVcXV3x7bffyo6Ni4vDrl27cOvWLTx//hw6Ojpo3749Jk2aBAcHB4V4YmNj8ddff+Hm\nzZt48uQJXr16BT09PXTo0AGTJk1Cnz59in0ff/31F7y8vBAVFYU3b95AT08PhoaGsLGxwccffwxj\nY2OF933kyBF4e3sjJiYG2dnZaNq0Kezt7TFjxgyl/70QERERERHR+6PchScXFxdERkYiKioKFhYW\nsLS0lO0r+ufiqKurw9bWFr6+vggICJBrCn3z5k25PxctPAUEBCA/Px/du3eXFZ0AYNOmTQgICECb\nNm3QsWNHaGlp4eHDh/Dx8YGvry82btyIwYMHy8Xw7bff4vjx49DR0UHXrl1Rr149pKamIiEhAR4e\nHnBycirzB+nNmzdj27Zt6Nq1K/r27Yv79+/j1q1bCA4Oxr59+9C5c2e542/fvo3p06cjPT0dzZs3\nx4ABA5CWloaAgAAEBATg2rVrWLduHUT/3xFs0KBBuH37NkJCQmBsbIyuXbvKrlX0z6VJS0vDjBkz\nEBoaCi0tLXTp0gUNGjRAcnIyQkJCEBMTI1d4OnnyJDZv3gxjY2O0bt0aXbp0wbNnzxAcHIyAgADc\nuXMHS5YsKfa18vLyMH36dNy5cwe2trYwNTXF7du34e3tjaCgIJw6dQr6+vpy5+zYsQM//vgjAKBz\n584wMjJCdHQ0PvroI7i5uSn1Hot69uwZpk6ditjYWNSvXx+dO3eGlpYWwsLCsGXLFly4cAH79u2D\ngYGBUtcTi8WYOXMmrl27Bh0dHdjZ2UFXVxe3bt3CqFGjYG9vX+r58fHxcHFxkd17sVgsdw/++usv\nLFiwAHl5eWjbti3s7e2RkpKCoKAg3Lx5E5999hm++OILuWt6eHjgyJEjMDU1hYWFBfT09PD48WNc\nvXoVV69excKFC/Hxxx/LnSMthGlqaqJz584wNDREeno6EhMTsX//ftjY2MgVnjIyMvDJJ58gODgY\nderUQfv27aGvr4/w8HDs2bMH58+fx759+9C8eXOl7iMREdF/iVCxX8xWky95iYiIlKaqubDchae1\na9fC3d0dUVFRcHR0LHWEU3Hs7Ozg6+uLmzdvyhWebt26hYYNG0IikcDPz0/uHGlRys7OTm77lClT\n8OOPP6Jhw4Zy2//++2/MmTMHy5cvR79+/VCrVi0AhaN4jh8/jqZNm+LIkSMK50VGRsLQ0LBM7wcA\n9u/fj8OHD8PKygoAUFBQgGXLluHQoUP45Zdf4OHhITs2JycHX375JdLT0/HRRx9hwYIFUFdXBwBE\nR0dj8uTJOHnyJLp06YLx48cDABYsWIBjx44hJCQEXbt2VXqEU1GLFi1CaGgoOnfujM2bN6Nx48Zy\nMd26dUvu+N69e2PAgAFo27at3Pb4+HhMnjwZ+/btg7OzMzp16qTwWqGhobCysoKvry8aNGgAAHj9\n+jU++ugjhIeHw9PTU24kU0REBDZt2gQNDQ24u7vL/V7s3LkTGzZsKNN7lUgk+PLLLxEbGws3NzfM\nnz8fOjo6AIDs7GwsXboUp06dwpo1a5S+l/v27cO1a9dgZGSEvXv3okWLFgCA3NxcLFy4EMeOHSv1\n/NOnT2PUqFFYsWIFtLS05PZFRUVhwYIF0NTUxK+//ipXdI2JicH06dOxdetWdO/eHT169JDtGzFi\nBGbOnKlQ9Llz5w6mTJkiK7w2adJEFuvOnTuhq6uLY8eOwcTERO68+Ph42e+i1NKlSxEcHIxBgwZh\n5cqVskKdWCzGTz/9hJ07d2LRokXYt2+fMreRiIiIiIiI3hPvtKrdu5AWj4qOcHr+/DliYmLQo0cP\n9OjRA9HR0Xj58qVsv7QoUvRDNwD06dNHoXgEFE5NGzRoEF69egV/f3/Zduk127VrV+x5lpaWskJJ\nWcyePVtWdAIANTU12eiUoKAg5OXlyfadPXsW//zzD4yMjDB//ny5D/pmZmayQt7u3bvLHEdJIiMj\ncfHiRdSuXRtbt26VKzoBgLa2tlyxAyicKvjfohMAtGrVSjYl8ty5c8W+nkgkwurVq+XuZZ06dTBt\n2jQA8j97APjzzz8hFovh7OwsV3QCgGnTpqF9+/ZKvtNCV69eRWhoKKytrfHtt9/Kik4AoKOjgxUr\nVqBBgwbw9vZGWlqaUteUFla++OILWdEJALS0tLBkyRLo6uqWen7dunXx7bffKhSdAGDbtm3Iy8vD\n/PnzFX4Obdu2la1k6OnpKbfP1ta22JFGnTp1gpubG/Ly8nDx4kXZ9oyMDGRnZ8PY2Fih6AQU/myL\nvrfY2FicOXMGRkZGWL9+vdzoMHV1dcybNw9mZmYICAjA/fv3S33/REREJVHVJaSJiIiUpaq5ULDm\n4qampjA0NERMTAyeP3+ORo0ayQpLdnZ2kEgksr41w4YNw4sXLxAdHY3GjRvD1NRU4XopKSm4fPky\nYmJikJ6eDrFYDKBwpAhQOIpDqnXr1qhduzauXLmCbdu2wdnZGUZGRu/8nvr166ewrWHDhjAwMEBa\nWhpevXqFRo0aAQACAwMBAM7OztDU1FQ4Tzoq5tGjR0hKSlIoEpXHtWvXABQW5MoyjTAnJwfXrl1D\nWFgYUlNTkZubC6CwUAjI39uimjVrJtcLTKp169YAgOTkZLnt0ntSdKpfUcOHD0d4eLjScV+9ehUA\nMHDgQLmpmVK6urqwsrLClStXEBYWht69e5d6vX/++QdPnjyBuro6hgwZorC/fv366NmzJ3x9fUu8\nRs+ePeV6mkkVFBTg2rVrEIlEcHJyKvZcW1tbAIUjyf4rIyMDV65cQWRkJNLS0mRFTunP5uHDh3Jx\nGhkZISoqCmvXrsX//ve/Yv9NSUnvY79+/eSKd1JqamqwsbFBdHQ0bt++XezPnIiI6G1UdSUfIiIi\nZalqLhR0VTs7OzucPHkSN2/exPDhw2UjYHr27AmJRAIA8PPzw7Bhw+SKUv918OBBrF27FllZWSW+\nVkZGhuzPenp6WL16NRYvXoxNmzZh06ZNaNy4MaytrdGvXz8MHToU2traZX4/JTXL1tPTQ1paGnJy\ncmTbkpKSAKDEnjja2towNDREUlJShRWepI3JpYUfZYSGhuLLL7/Es2fPSjym6L0tqmnTpsVulxZe\npAUsKelrlHRPyto/6PHjxwCA9evXY/369aUem5KS8tbrSX9mjRo1KrZYCJT8O/C2/a9evZLdx+J+\nx4tKTU2V+7uvry++/fZbvHr1qsRz3rx5I/f39evXY86cOfDw8ICHhwfq16+PTp064YMPPsDw4cNR\np04d2bHS++jp6akw2uq/lLmPRERE1dHDhw+xcOFCvHr1CnXr1sW6devQqlWrYo998OABXFxcMHHi\nRCxYsKBqAyUiIqoklZULBS089ezZU67wdOvWLbRs2VL24dzY2FhWcCqpv9Pdu3exfPlyaGho4Jtv\nIedWFAAAIABJREFUvoG9vT2aNGmCWrVqQSQS4aeffsLvv/8uK2RJOTk5oWfPnrh48SICAwMREhIC\nHx8f+Pj4YMuWLfD09CyxcFKS4kbVVCeiMo6zy8rKwqxZs/DixQuMGTMGEyZMQMuWLVG7dm2oqanh\n+vXrmDp1aonnC30/pKPebG1t3zqi7W0Fo6JKu49ve8/FjRgC/o1VXV29xBFfxXn27BnmzZuH7Oxs\nzJgxA0OHDoWRkRF0dXWhpqYGLy8vfPfddwq//zY2Nrh48SIuXbqEgIAAhIaG4vLly7h06RLc3d2x\ne/dutGvXTi629u3bw8zMrNR4ipuWSUREpAyRqIIbqpbxYsuWLcPEiRMxYsQInDx5Et999x3++OMP\nhePEYjGWLVsGR0fHCoqUiIiokKrmQsFHPAGFvZsSEhKQmJgoa6Qt3e/l5YX4+PgSC0/nz5+HRCLB\npEmTii2CPHr0qMTX19fXh4uLC1xcXAAACQkJWLJkCfz9/fHjjz9i48aN7/weSyIdwfTkyZNi9+fk\n5MimolXEaCfg3+JK0WlXpQkMDMSLFy/Qvn17/PDDDwr7S7u35dG4cWM8fvwYiYmJciuqSZV0r0oi\nLRw6OTnB1dX1neOTNpxPTk5GXl5esaOepKPKyqpevXrQ0dGRNT2vXbu2UuddunQJ2dnZGDRoEObO\nnauwv7SfUa1atTBkyBDZtMHk5GSsWbMGZ86cwffff4+DBw8C+Pc+du/end/qEhGRSnr58iUiIiJk\nC8EMGzYMK1euREpKikJ7gu3bt6Nfv37IzMxEZmamEOESERFVuMrMhe80JEX6wTs/P79c5zdu3Bgm\nJiZ4+vQpDhw4AEC+sCT9s5eXFxITE9G6dWuFIoy0KbR0xa6iUlJSFFbGK42xsbFslbWoqKiyvZky\n6tatG4DCVc6Ku3/Hjx+HRCJBy5Yt5d7zu9xzaQ+jixcvKjUlSnpvSxr5dfr06TLHUBrpPTl16lSx\n+729vct0vT59+gAoufl5WTVr1gxGRkYQi8XFXvPVq1e4ceNGua6toaEh+3338fFR+rzSfv9zc3Nx\n/vx5pa9laGiIr776CoD877/0Pl68eLHc/9aJiIiEIu3RWPS/9PR0hWMaN24sW+xFXV0dhoaG+Oef\nf+SOi4qKwvXr1zF58uSqCp+IiOidCZ0L36nwJC2IPHjwoNzX6NmzJ4DC/jFqampyK9b16NEDIpFI\n1lemuN430n5FJ0+elOtjk5GRgcWLFyvcTACIiIjAmTNnkJ2drbDv77//BlC2qVflMXjwYDRt2hRP\nnjzBxo0bUVBQINsXGxsLd3d3AMCUKVPkznuXe96uXTvY29vjzZs3mDVrlkJz75ycHFy5ckX2d+m9\nvXXrFuLi4mTbCwoKsGXLFoSEhJQ5htK4urpCTU0Np06dkosDAPbs2YN79+6V6XqOjo5o3749AgIC\n8N133xXbA+n58+c4dOiQ0td0c3MDAGzatEludFNubi5WrVr1Tt98fv7559DU1MQPP/yAv/76S2F6\nnEQiwd27d3H9+nXZNunP6Pz583jx4oVcPCtXrpT1ZyoqMTERhw8fLrY3V3G//+3bt4ejoyMePXpU\nYr+vtLQ0HDx4UKEw5eTkBCcnJ9y9e1eZW0BERO+xylrJx9XVFQ4ODnL/7d27t8zx5eXlYenSpVix\nYoXcasREREQVRVVz4TtNtevduzdq1aqF8+fPw9XVFcbGxlBTU0P//v3h4OCg1DXs7Ozg6emJnJwc\ntG/fHnXr1pXtq1evHiwtLREREQHg3yJVUaNGjcLevXsRHh4OR0dHdO3aFRKJBEFBQdDU1MTo0aNx\n9OhRuXOePn2Kr776CrVq1UK7du3QtGlT5OXlISIiAo8fP0bt2rUxZ86cd7gzb6etrY2ff/4Z06dP\nx+7du+Hr64sOHTogLS0N/v7+yMvLw4gRIzBu3Di586ytrdGoUSOEh4dj1KhRaNu2LTQ0NNClSxeM\nHj36ra+7du1aTJs2DcHBwbL7Vb9+fSQnJyMqKgp16tSRFR/at28Pe3t7XLp0CSNHjkT37t1Rp04d\nhIWF4Z9//sG0adOwc+fOCrsnVlZW+PLLL/HTTz9hxowZ6Ny5M5o1a4bo6GjExsZi0qRJ2Ldvn9LX\nU1NTw9atWzF9+nR4eXnh9OnTsLCwQNOmTZGTk4P4+HjExsaiQYMGGDt2rFLX/PDDD3Hjxg1cv34d\nQ4YMQY8ePVCrVi2EhoYiOzsbI0eOxIkTJ0psPl6aDh06YN26dVi8eDHmzp2LjRs3wtTUFAYGBkhN\nTUVkZCRevnyJ6dOny0av9e/fH+3atUNERAQGDhwIW1tbaGtrIyQkBBkZGcXes/T0dCxZsgQrVqyA\npaUlmjdvjoKCAsTFxSEmJgaampqYP3++3Dnr1q3DzJkzceHCBVy9ehUWFhay0V+PHz/G/fv3IRaL\n4eLiAg2Nf/+3Ip3WWVrjfyIiosrk6ekp61copa+vL/f3pk2bIikpCWKxGOrq6hCLxUhOTpYb9f38\n+XMkJCTgk08+AVCYTyUSCTIyMrBy5crKfyNERETlJHQufKfCU6NGjbBt2zb8+uuviIyMRHBwMCQS\nCZo0aaJ04al79+5QU1NDQUFBsSOa7OzsEBERATU1Ndly8kUZGBjg6NGj2Lx5M27cuIHLly+jQYMG\nGDBgAObMmQMvLy+Fczp16oR58+YhICAADx48QHh4ODQ1NdG0aVNMmTIFbm5ub21GXRGsra1x4sQJ\nbN++HdeuXcP58+eho6MDa2trjB07Fs7OzgqNrLW0tLBz505s2rQJt2/fRmRkJAoKCiAWi5UqPNWt\nWxf79+/HoUOHcPr0ady9exe5ublo2LAhunbtCmdnZ7njf/nlF+zZswenTp1CQEAAdHV1YW1tjY0b\nNyI7O7tCC08AMGPGDJiYmGD37t2IjIxEdHQ0rKyssHv3bqipqZWp8AQUTkE7cuQIjhw5grNnzyI6\nOhp3795F3bp1YWhoiI8//hgDBgxQ+noaGhr47bff4OHhgePHj+PGjRvQ19eHnZ0dvvrqK/z2228A\nCoum5TF06FB06NABf/zxB/z8/BAYGAgAaNiwISwtLdG3b18MGjRILp59+/bht99+g6+vL27cuAED\nAwPY2tpi1qxZuH37tsJrtGjRAosWLUJAQABiY2MRGxsLkUiExo0bY9y4cfjwww/Rpk0buXP09PSw\nZ88eeHt7w9vbG+Hh4QgPD4e+vj4MDQ0xbtw4ODg4lGs1SCIiokIVu4S0lDKLxTRo0ACWlpY4ffo0\nRowYgdOnT8PS0lKup0WzZs3g7+8v+7u7uzsyMzPZ/5CIiCqQauZCkeS/83mIqFzy8/MxbNgwPHz4\nEEePHoWVlZXQIamchy8Up8dS2VVGMqsMGdnVv6eYUf1aQofwVrW0qv+UoItRyW8/qBq49SRN6BDe\nKurZa6FDUMrhyV0UtqVnF6Ain0pFIkBfR/muEnFxcVi4cCHS09Ohr6+PdevWoXXr1pg+fTrmzJmD\nDh06yB0vVOEp8VVulb6eqsrOFb/9IIE1q1f9cwxQ9lWzhPDqTZ7QIbzVrGNhQofwVnEJiq1LqqOE\nB0lCh/BWL/dOKHa7quZCFp6IyigyMhJt2rSRm06XmZmJ9evX48CBAzAzMytzI3RSDgtPFaMGPB8C\nYOGporDwVHFYeKo4xRWeXudU/MN2He13amdaLbHwVDFYeKo4LDxVDBaeKk5NLjypai58p6l2RO+j\n77//HrGxsbCwsECjRo2QkpKCqKgopKamQl9fH2vWrBE6RCIiIiIiIqJqgYUnojIaN24cvL29ERMT\ngzt37gAonDM7ePBgTJ06Fc2bNxc4QiIiopqnogdN1IBBGERERHJUNRey8ERURiNHjsTIkSOFDoOI\niEj1VJcnZCIiIqGoYC4UfrIfERERERERERGpJI54IiIiIiLBVfQC0ir4hTEREak4Vc2FHPFERERE\nRERERESVgiOeiIiIiEhwFb0ke3X5lpeIiEhZqpoLWXgiIiIiIsGp6ko+REREylLVXMipdkRERERE\nREREVCk44omIiIiIhFddvpYlIiISiormQo54IiIiIiIiIiKiSsERT0REREQkOFVdQpqIiEhZqpoL\nWXgiIiIiIsGp6ko+REREylLVXMjCExERERFVC9XlAZmIiEgoqpgLRRKJRCJ0EEREREREREREpHrY\nXJyIiIiIiIiIiCoFC09ERERERERERFQpWHgiIiIiIiIiIqJKwcITERERERERERFVChaeiIiIiIiI\niIioUrDwRERERERERERElYKFJyIiIiIiIiIiqhQsPBERERERERERUaVg4YmIiIiIiIiIiCoFC09E\nRERERERERFQpWHgiIiIiIiIiIqJKwcITERERERERERFVChaeiIiIiIiIiIioUrDwRERERERERERE\nlYKFJyIiIiIiIiIiqhQsPBERERERERERUaVg4YmIiKiKXb16VegQ3urAgQPIzMwUOowaLSMjA+Hh\n4Xjx4oXQoRARVTvMhe8H5kICAJFEIpEIHQQREdH7xMLCAi1btsSECRMwatQo6OvrCx2SAgsLC9Sp\nUwcjRozAhAkTYGpqKnRINcr27duxZcsW5OXlQSQSYeTIkVi+fDm0tLSEDq3cwsLCkJ2dDQDo1q2b\nwNEQUU3HXKj6mAtJioUnIiJ672zbtk32zduSJUuq/PVHjRqFiIgIiEQiaGtrY+jQoZg4cSLat29f\n5bGUZPbs2bh06RLy8/MhEonQvXt3uLq6wsHBAWpqHDBdmrNnz+Krr74CANSuXRvZ2dkoKChA9+7d\n8fvvv0NbW1vgCMtn8ODBiI+Ph0gkQkREhNDhENE7Yi58O+bC8mMupKJYeCIiqgbS09MRHx+Pxo0b\no3HjxkKHU6qXL1/CwMAAGhoaQodSboMHD8bDhw8hEokQGRkpSAx37tyBp6cnzp07h9zcXIhEInTs\n2BETJ07E4MGDq8W3gUlJSTh48CAOHz6MFy9eQCQSwdDQEOPGjcPYsWPRsGFDoUOsliZOnIjQ0FD8\n8MMPGDVqFP755x/MnTsXoaGhsLOzw7Zt22rkA7eTkxPi4+MBAFFRUcIGQyqppuRCVciDAHOhspgL\ny4e5kIpi4YmISGC//fYbtm7dKvs2zdnZGd9//321S8YnT57Ehg0b8PLlS9SqVQuTJ0/G7NmzIRKJ\nhA6tzH744QckJSUBAH755RdBY0lNTcXhw4dx8OBBPH36FCKRCHXr1sWYMWMwfvx4GBkZCRofAOTn\n5+P8+fPYv38/goKCIBKJoKGhgQEDBmDixImwsbEROsRqpXPnzmjbti0OHTok25aVlYWPP/4Yt2/f\nhqmpKRwcHKCrqwsLCwv069cPXl5eSE1NBQB8+umnQoVOJJiakAtVKQ8CzIVlxVxYNsyFVBQLT0RE\nAjpz5gzmzp0LADAwMMCbN28gFothY2ODHTt2QEdHR+AIC924cQNTp06V2yYSiTBkyBBs2LCBw80r\ngEQiweXLl7F//37cuHEDEokEampq6NOnD1xdXdG7d2+hQwQAREdHY//+/fD29pY1XG3bti1cXV0x\nYsSIavM7K6QOHTpg4MCB2Lhxo9z2jIwMzJw5E4GBgQAg63exZs2aajHygEgoNSEXMg9WDeZC1cFc\nSEWpL1++fLnQQRARva+WLl2KpKQkrFu3Dj/++CNGjx6NsLAwBAUFITQ0FIMHD64WQ/mXLVuGJ0+e\n4IsvvsCvv/4KR0dHBAcHIzAwEPHx8Rg4cGCN/ca3uhCJRDAxMYGjoyPy8vIQEhICiUSC+Ph4eHt7\nw8fHB82bN0fLli0FjbNBgwYwMzNDZmYmwsLCABROO7ly5Qq8vLygp6cHKysrQWMU2vHjx5Gfn48x\nY8bIbdfS0oKLiwtat24NIyMjmJmZwcbGBhYWFnj69CmaN28OMzMzDBw4UKDIiYRRE3Ih82DVYC5U\nHcyFVBRHPBGRyqoJvSI6d+4MMzMzeHl5ybZlZ2djypQpCAkJQcuWLWXDkC0tLeHg4ABPT0/ZMORZ\ns2ZVSZw2NjYwMjLCyZMnZdtSUlLg5uaGhw8fwsbGBk5OTqhduzaMjY3RpUsX+Pr6IiMjAwAwcuTI\nKokTAOLi4mrkqjNxcXE4cOAATp48KbtvPXv2RK9evXDq1ClERUVBJBJh3bp1GD58uCAxXr16Ffv3\n78e1a9dQUFAAHR0dODs7y2K8dOkSJBIJFixYgMmTJwsSY3WwaNEinDp1ChcvXkSTJk2EDqfMYmJi\ncPv2baSkpKBNmzZwcHAAABQUFCA/P79a9Fwh5TEXVoyalAcB5sLKxFyoHOZCkiMhIlJBW7dulVhZ\nWUksLCwklpaWkm+++UaSnZ0tdFgKOnToIJk7d67C9oyMDMmHH34oMTc3l5ibm0ssLCwkCxculEgk\nEomTk5NsW1Vp3759sXEmJydLhg4dKovnv3FKt1UlCwsLyUcffSQ5f/68RCwWV+lrl1V+fr7k7Nmz\nkkmTJkksLCwk5ubmks6dO0u+//57SVxcnNyx58+fl3To0EEydOjQKo3x1atXkp07d0oGDBggi9HB\nwUGya9cuSVpamtyxYWFhki5dukgcHR0rLZ7Y2FjJnDlzJIMHD5ZMnTpV4ufnV2mvVV4BAQESc3Nz\nydKlS4UOpUwSExNlv4v//fcskUgkBw8elFhYWFTLe07FYy6sODUpD0okzIUVjbmw7JgLqSjh528Q\nEVWwM2fOYPPmzQD+7RVx6tQpPH36tNr0ipBq0qQJEhMTFbbXrl0be/bsgY+PD+7du4esrCxYW1sD\nAAYMGIDnz59XaZyGhoZITk5W2N6oUSMcOXIEf/75J8LDw5GVlQVLS0sAhXP7hVjppUGDBrh16xb8\n/f2r7aozSUlJOHToEA4fPoznz59DIpGgZcuWcHNzg4uLC/T09BTOGTBgAPr27YtLly5VSYx3797F\n/v37ce7cOeTk5EAikaBnz55wc3ODvb19sVNKrKys0LdvX/j4+FRKTPHx8Rg/fjwyMjIgkUjw4MED\n+Pn5YcmSJZg4cWKlvGZ5dOvWDfv27UNBQYHQoShNOnLj6dOnsmkP+/fvlzvGyckJ33//PS5evAg7\nOzuBIiVlMRdWrJqUBwHmworCXFh+zIVUFAtPRKRy/vjjD4hEIqxduxYjRoxAUlIS5s2bh8DAQMyY\nMQO///57tXng7tq1q+yDQLNmzeT2iUQiODk5wcnJSW67tAFrVbK2tsaFCxeQkpKC+vXry+3T0dHB\ntGnTFM5Zv359VYUn5/Lly/Dx8cH+/fsRHBwMd3d3/Pbbb9Vq1RkHBweIxWIAwAcffAA3Nzf06dPn\nrefp6+sjPz+/ssMDAIwdOxYAUKtWLYwfPx5ubm5KTduoVauW7L1VNHd3d7x+/Rp9+vTB+PHjERcX\nh61bt2LlypWQSCRwdXWtlNctj27dugkdQpls374dT58+xfTp0zF37lyIRCKFh20DAwOYm5sjODhY\noCipLJgLK1ZNyoMAc2FFYS58N8yFJMUeT0SkcmpCrwipoKAguLm5YcyYMVi1alWVvW5ZXblyBTNm\nzMDUqVMxf/58ocNRWnR0NDw9PWWrzohEIrRp0waurq4YPnw4dHV1BYmra9euGD16NFxdXcvUIPXV\nq1d48+ZNlSwrPXDgQLi6umL06NHFfusshN69e0NDQwMXLlyApqYmAOD27duYMmUKsrKy8Mknn8DZ\n2Rm6urrQ1dVF3bp1kZycLPuA8t8PtPSvQYMGQSwW48KFC7Jv8C0sLODi4oI1a9bIjpszZw6Cg4Nx\n48YNoUIlJTEXVqyamgcB5sJ3wVz4fmEurDwsPBGRyunYsSMGDBigsHzrmzdv8Nlnn8Hf3x9A9Vm+\nNSQkBAUFBdXi28fSeHt7QyQSYdiwYUKHUmYZGRk4ceIEDhw4gLi4OIhEItSuXRsjR47EhAkTqrwB\na2ZmpmAP+jWZlZUVHBwcZNOHpIKCgvDJJ58gKytLts3FxQWrV6/G4MGDER8fD5FIhIiIiKoOWU5w\ncDA8PT1lzUqdnZ2xcuVKAMDNmzcREBAAV1dXQabCdOzYEf369cMvv/wi21bcw/bcuXNx/vx53Lt3\nr8pjpLJhLqx4NTkPAsyFqoK5sPIwF1YeTrUjIpVTE3pFFNWlSxdBXresnJ2dhQ6h3PT09ODm5gZX\nV1f89NNP2LFjBzIyMvDnn3/C09MTffr0wbx582BmZlYl8fBBu3zq1KmDnJwche02Njbw8vLChg0b\nEB4ejszMTNlqMxKJRPafkH799Vf8+uuvcr0uik4V0dXVxbZt29CwYUNBpkno6Ojg9evXbz0uMTER\n+vr6VRARvSvmwopXk/MgwFyoKpgLKw9zYeVh4YmIVE5N6BVBVSs9PR1Hjx7FwYMHkZCQAAAwMTFB\nz549ce7cOVy5cgV+fn7YunUrPvjggyqP7/Xr17ImocURelh8ZmYmEhISSo2xKvo4tGvXDsHBwcjN\nzVVYxrht27bYvn27wjnnzp2r9Lje5sqVK3B3d0fjxo2xYMEC2NjYKPQx6dSpE+rXr4/Lly8L8rDd\ntm1bhIeH4/Xr16hTp06xxyQlJSEqKqrG9ex4XzEX0n8xF74b5sJ3w1z4fmPhiYhUzujRo3H8+HFs\n3bq12vaKKMnjx48RFhaGTp06yfUuiIqKwooVKxAVFYXmzZvj66+/Rt++fQWLMzU1FY8fP0bz5s3l\nmqwmJSVhw4YNuH//PoyMjDBnzhy0a9dOsDjDwsKwf/9+nD17Fjk5ORCJROjbty/c3NzQu3dvAMDC\nhQvh6emJ9evX4+eff66yh+1Xr15h8+bNOH/+PFJSUko8Tshh8Y8ePcIPP/yAGzdulLoqTVXFOGjQ\nINy4cQPHjx/HuHHjKv31KsrevXuhpaWFXbt2oU2bNiUeZ2FhgUePHlVhZP8aNmwYVqxYge+++w7r\n1q1T+DBTUFCAVatWITc3F8OHDxckRiob5sLKVVPyIMBc+K6YCysGc+H7jYUnIlI50qVPa9LyrVIe\nHh44cOCA3BK8GRkZ+Pjjj2UNX2NiYjBr1iycOHGiyvsxSG3fvh179uzB8ePHZQ/cubm5mDBhAv75\n5x9IJBLExMQgODgYp06dQtOmTassttzcXJw+fRoHDhzAvXv3IJFIUKdOHYwfPx6urq5o0aKF3PGa\nmpqYPHky/Pz8ZD1PKltaWhrGjh2Lx48fQ11dHTo6OsjKykKjRo3w4sULSCQSiESiKr1v//Xs2TOM\nHz8eqampMDQ0hFgsxsuXL2FtbY2EhASkpKRAJBLB2toaGhpV8zgxcuRI3L17V/ZNfU1x7949dOrU\nqdQHbQCoV68eQkJCqigqef/73//g7e2Ns2fPIiwsDP369QNQ+P+bDRs2wNfXF48ePYKtrW2Nn270\nvmAurFzVOQ9KY2EufHfMhRWHufD9piZ0AERElaFLly7VukFpSQIDA2FqagpjY2PZtpMnTyI1NRVD\nhw7FhQsXsHDhQuTl5WHfvn2Cxenv748WLVrAwsJCtu2vv/7C06dP0b17d3h4eGDSpEl4/fo1/vzz\nzyqN7YMPPsC3336LsLAwmJqaYtmyZbh69SoWLlyo8KBdVKNGjZCbm1slMe7YsQMJCQkYNWoUgoKC\nMGjQIIhEIly7dg0hISH4/vvvYWBggK5du+Lvv/+ukpj+a/v27UhNTcVnn32Gq1evok+fPhCJRDh4\n8CD8/Pywa9cuNG/eHJqamti9e3eVxKSlpYVVq1bVuBWlsrKyFJZfL056enoVRFM8DQ0NbN++HYMH\nD8aTJ09k/27v3buHXbt24dGjR3B0dMTWrVtlK/1Q9cdcWHmqcx4EmAsrCnNhxWEufL9xxBMRUTXy\n/PlzdOrUSW7b9evXoaamhkWLFqFhw4aYPHkyjh49isDAQIGiLJxKUPRhGwAuX74MkUiElStXokWL\nFrCzs8Ply5dx7dq1Kn04Sk9PR//+/eHm5gY7Ozulz5s2bRpGjBhRiZH969KlS6hfvz6WLVsGLS0t\nuYeXWrVqYezYsbC0tMS4ceNgbW0tSJ+D69evo2nTpiUuqd6rVy/s2rULQ4cOxc6dOzFz5swqjrDm\naNSoEeLj4996XFxcnKA9TPT09LBp0ybMmjULV69exePHjyEWi9G0aVP06dNH8OlC9P6oCbmwOudB\ngLmwojAXVhzmwvcbC09EpPJqQq8IqYyMDIVmhnfu3IG5ubncsrKmpqa4fv16VYcnk5aWhnr16slt\nu337NkxMTOS+SbW0tMStW7eqNDZfX1+5n7OyTExMYGJiUgkRKUpMTIStra1C7wCxWAx1dXUAQIcO\nHdC1a1ccPXpUkIftZ8+eoVevXlBTKxwcLf1AkJeXB01NTQCAsbExbG1t8ddffwn6sF1QUICrV68i\nNDQUqamp6NixI8aMGQMASElJQVpaGoyNjWX3tqp1794dJ06cwM2bN0v8AHju3DkkJibCzc2tiqNT\nZGpqKtg0Xqo8zIUVqzrnQYC5sKIwF1Yc5sL3G6faEZHK8/DwwLx58yAWi2XbpL0iQkNDkZWVJesV\nERcXJ2CkhctcJycny/7+4MEDpKSkoHPnznLHqampCdq3Q0dHR9ZnAwCePn2KpKQkheWwNTU1kZeX\nV6WxledBu6qpqalBT09P9nfpktJF7ykAGBoaKvXtYGXQ1taW+zAgjfG/zV8NDAzw5MmTKo2tqPDw\ncAwePBgzZ87E77//jsOHDyM4OFi238/PD0OGDMGVK1cEi3Hq1KlQV1fH7NmzceTIEblpBLm5ufD2\n9sZ3330HHR0dfPTRR4LFSaqNubBiVec8CDAXVhTmworDXPh+Y+GJiFReTegVIWVubo7Q0FBZw8jD\nhw9DJBLB1tZW7rgnT56gUaNGQoQIAGjTpg1CQkJkD17e3t4QiUQKvUSePXuGBg0aCBEiHj16hHXr\n1mHChAkYNGgQ1q9fL9t3584deHl5CdZHwNDQEP/884/s79IPCOHh4XLHxcXFKXwTXFUaN25w+I7d\nAAAgAElEQVQsF2PLli0BAKGhobJtEokEERERch8cqlJiYiKmTJmCR48eoW/fvpg/f77CEtcODg7Q\n1NSEr6+vIDEChf9eVq9ejezsbCxduhQ9evSASCSCt7c3rK2t8c033yAzMxOrVq0qtfdKZXr8+DHO\nnDmDxMREue1RUVGYMGECOnfuDGdnZ0E/tNC7YS6sWDUhDwLMhe+KubDiMBe+31h4IiKV9/z5c4Vv\n/or2imjRogUmT56Mtm3bCto3CQDGjRuHvLw8uLi4wMXFBXv27EGDBg1gb28vOyYjIwORkZFo27at\nYHGOGDECWVlZGDNmDGbNmgV3d3fUrl0bjo6OsmNycnIQERGB1q1bV3l8hw8fxrBhw+Dh4SH78FL0\nG9SsrCwsX74cFy5cqPLYAKB9+/Z48OCBbOSBnZ0dJBIJfvzxR8TFxSEjIwPbt29HVFSUQg+RqtKh\nQwfExsYiJycHAGRLa69ZswZXrlzB/fv3sWLFCjx69AhWVlaCxLht2zakpaVh6dKl2LZtG6ZOnapw\nTK1atWBhYYGwsDABIvzX8OHDcfjwYTg6OkJbWxsSiQT5+fnQ0NDABx98gP3792PYsGGCxVeTRsNQ\n+TAXVqzqngcB5sKKwFxYsZgL318sPBGRyitLr4ikpKSqDk/O0KFD8fnnn0MsFiMyMhLNmjXDzz//\nDG1tbdkxZ8+eRV5ensI3v1Vp3LhxGDlyJJ4+fQpfX19oa2vjhx9+kPu27+LFi8jKykK3bt2qNLbg\n4GAsW7YM2tra+Oabb3Do0CGFb/5sbW1Rp04dwVbJ+eCDD5CWloZr164BKOwBYm9vj5iYGAwbNgzd\nunXDpk2bIBKJ8PnnnwsSY79+/ZCTk4NLly4BAFq1aoUxY8YgKSkJn376KUaOHImDBw9CQ0MDX331\nlSAxXr9+Haampm/t+2FkZITnz59XUVQls7S0hLu7O4KDg3Ht2jVcuXIFwcHB2L59Ozp27ChobDVp\nNAyVD3NhxarOeRBgLqwozIUVj7nw/cTm4kSk8krqFeHk5CR3nNB9k6Rmz56NGTNmICMjo9hlZ3v1\n6oUTJ04INgwZKLxXa9euxZw5c/Dy5Uu0bt0atWvXljvGxMQEW7ZsgbW1dZXGtnPnTohEIuzYsUOh\nH4iUmpoaLC0tBfu2atiwYbCzs5P7gLJx40Zs3LgRPj4+ePXqFVq3bo3PP/9ckA8sADBo0CCF6Q7L\nly+HiYmJXIwzZsyAubm5IDG+ePECDg4Obz1OIpHgzZs3VRCRctTU1ASdKlucmrCKGL0b5sKKVZ3z\nIMBcWFGYCysPc+H7hYUnIlJ5RXtFGBsbV8teEf+lpaVV7IM2ADRr1kzQZWaLKi0WS0tLWFpaVnFE\nhasKdejQocQHbamGDRvi3r17VRSVPA0NDTRu3Fhum66uLpYuXYqlS5cKEpMy1NXVMWXKFEyZMkXo\nUAAUfpB++fLlW497/PixwupT1YW/vz+ioqJgZGSE/v37y1ZOqmo1YRUxejfMhZWjOuZBgLmwMjEX\nVjzmQtXHqXZEpPJqQq8IqjivX79GkyZN3npcZmam3Bx+qnnatWuHe/fuyY3i+K8HDx4gKipK0OH7\nR44cgbOzM4KCguS2L1u2DJMnT8batWsxe/ZsTJkyBfn5+YLEWBNWEaN3w1z4fmEufH8wF1Yc5sLK\nwxFPRKTyhg4digcPHmDXrl2IjIyEkZER1q1bVy16RSxatKjc54pEIqxevboCoynZli1byn1uVfdm\naNCggVJLGj98+FDhm1aqWUaPHg0/Pz98/fXX2Lx5s8I3uRkZGfjuu+9QUFCAMWPGCBQl4OPjg2fP\nnsk98EtXk9LV1YW9vT1CQ0Ph7++P06dPY+TIkVUeY00cDUNlw1z4bmpSHgSYC98nzIUVh7mw8rDw\nRETvheraK+L48ePFbheJRACg0Ai06PaqLjyJRKIS4ymJNM6qfODu0qULfHx8EBYWhg4dOhR7zI0b\nNxAfH4///e9/VRJTTfjAUhM++P3X0KFDce7cOVy4cAGOjo6yHiB37tzBl19+iZs3byItLQ1DhgxB\nv379qjw+qdjYWJiZmcktB3769GmIRCJs3LgR9vb2SElJgYODA44ePSrIw/a4ceMQEBAAFxcXGBsb\nIyoqqsTRMH379q3y+KhiMBeWX03KgwBzYXkxF1Ye5sL3GwtPRPTeqI69ItasWaOw7e7duzhw4AAM\nDQ3h5OQkW/46MTERPj4+SEpKwsSJE0t8kKwMs2bNUtj25MkTnDhxAjo6OujVq5dcnH5+fv/H3p3H\n1Zj3/wN/XTqhpM3SYvtStApRthJp7EtlnSg0M24Gw8xgmBlkG4x1RgwxxpJdGxNRQsqWbVTKyFra\n0K5Sna7fH/0646gUnXNd1+m8n4/H/fjenXP17XXbXp9zLe8PioqK4OLiUmn7bnmbOnUqQkJCMGfO\nHKxatQp9+vSRej86Oho//vgjRCIRJk+ezEmm6j6wfEjF8Vwttqv74FcbfC22AWDz5s3YsmULfH19\ncfHiRQDlt8Y/fvwYIpEIU6ZMwYIFC3jJViE7O7vSbfo3b96Epqam5EOArq4uunfvjocPH/KQUNh3\nwxDZoi78NIrUgwB14aeiLpQf6kLlxrAf8zefEEKIXD148ADjx4/H+PHjsWDBAqmrQgBQUlKC9evX\n49ixYzhy5AhMTU15yZmSkgJXV1f07NkTy5Ytq/QhJjMzE8uXL8f169fh5+fH+aJ7z549+PXXX8Ew\nDDQ0NCTDIkUiEbKyssCyLBYtWoSpU6dykqcuV3mBqj/wyFpdFtsA4OLiIqMknyYnJwfXr19HUlIS\nxGIxDAwM0KdPHzRr1ozXXABgZWUFBwcHbN26FQBQVFSEHj16wM7ODjt27JAct2DBApw9exb37t3j\nKyqKi4urvRsmJSUFubm5aNOmTaXduwiRJUXoQqH3IEBd+CmoC+WHulC50YknQki9o4i3SVeYNWsW\nHj58iLNnz1Z7+z7Lshg8eDCMjY2xfft2jhOWW7hwIa5du4awsLBKHwgqFBcXw8nJCba2ttiwYQPH\nCYFLly7B29sbMTExUq936tQJc+fOrdXWw4TIwqBBg1BWVoawsDAAwPnz5zFr1ix89913mD59uuS4\nGTNmIDY2lnbKITJBXShfitCDAHUhEQ7qQuVGj9oRQuodRZgVUZ1bt26hb9++H5wZwTAMOnfuzGsh\nX7lyBba2ttUutoHyxzm6d++Oq1evcpjsPw4ODnBwcEBWVhaSk5NRVlYGfX19GqKqRLKysqCpqQkV\nFRVec/Tp0wdHjx7FqlWrYG9vj40bN4JhGKmZEQCQkJAAAwMDnlKS+oa6UL4UoQcB6kJCXUiEgU48\nEULqHUWYFVGdwsJCZGZm1nhcZmYmioqKOEhUtdzcXLx586bG4woKCpCXl8dBov9UfGiqoKOjU2mH\nlwqZmZnVzjrhUllZGbKzswEA2traaNCgAc+JKsvIyEBaWhoAQE9PTxAfWuLj4xEZGQlHR0cYGRlJ\nXo+MjMRPP/2EjIwMNG3aFPPnz8f48eN5yzlz5kyEhobC19cXBw8eBMuyGD58uNSW9QkJCUhLS8Og\nQYN4ywkA6enpOH/+PJ4+fYr8/PwqZ7HwfVKC1A51oXwJuQcB6kJ5oS78dNSFyo1OPBFC6p33n69/\n8OABvLy84O7uXuWsiAULFkhmRfBZyADQoUMHREdHIzY2FpaWllUeExsbi+joaHTq1InjdP9p06aN\nZIZAdbsfJSUl4dq1a5zvjrRixQosW7asxuOys7Mxbdo0BAUFcZCqahEREdi3bx9u3bqFt2/fAgAa\nNWqE7t27w8PDQxA7phw+fBh79+7F8+fPpV5v27YtPDw8MGnSJJ6SAQcOHEBgYCBGjRolee3Vq1eY\nM2cOCgsL0aBBA+Tm5sLLywumpqZSWzhzSU9PD/7+/jh69ChevXoFKysruLq6Sh0THx+P/v3787rY\n3rt3LzZu3IjS0lLJaxWLbSHdDUNqh7pQvoTcgwB1oaxRF9YddaFyU/Hy8vLiOwQhhMjTsmXL8Pbt\nW/j4+EAkqny+XUVFBXZ2dggICMDDhw8xfPhwHlL+l+X8+fOSHTMMDAygoaEBlmXx4sULHDx4EMuX\nL0dRURG++eYbWFhY8JKzqKgIERERCA0Nhba2NoyNjSVXJktLS3Hq1CksWrQIOTk58PT0hLW1NWfZ\nZsyYIVmwVic/Px/Tpk1DQkICJ8NKq7J69Wr88ssveP78OUpLS9GgQQMwDIOSkhIkJSUhODgY2dnZ\n6NevHy/5xGIx5s6di7/++gvZ2dlo0KABWrZsiSZNmqCgoADZ2dm4fPky4uPjMWTIEF6uTG/YsAH6\n+vr44osvJK8dOnQIERERmDJlCnx9fWFubo7g4GC8ffsWn332GecZK2hoaKBnz54YMGAAzM3NKz1C\nZGZmhhEjRvCyoxgAXL58GT/88APU1dUxbdo0iMVipKWlYcWKFejQoQNevnyJ3NxceHh4YOLEiTAz\nM+MlJ/l01IWyJeQeBKgLZYW6ULaoC5UX3fFECKn3FGFWRIVx48YhLi4OR44cgbe3N7y9vSWLmLKy\nMgDlV1kmTJiAcePG8ZZzypQpiI6OxsWLF7F48WL89NNPaNmyJYDy29DLysrAsiwcHBwwbdo0TrN1\n6tQJmzdvhqGhYZUfnAoKCvDll18iLi4OI0eO5DRbBX9/fxw4cABNmjTB1KlTMXr0aMk8g9TUVJw8\neRJ79+7FwYMHYWZmhjFjxnCecf/+/QgNDYWenh7mzp2LESNGSO6QKCkpwd9//43ffvsN4eHh2L9/\nP+e/z0D54yHvf5i7evUqRCIRZs+eDZFIBCcnJ1haWuKff/7hPJ8iOXDgABiGwZ49e2BlZYXFixfj\n7t27kjtf5s6di5UrV+LEiRPw9/fnOS35FNSFsiXkHgSoC2WFulC5UBfKj/AenCWEEBlThFkR7/Ly\n8sL27dtha2sLVVVViMViiMViqKqqwtbWFt7e3li+fDmvGUUiEf744w/89NNPaNWqFcRiMVJTU5Ga\nmgqxWIxWrVph8eLF+OOPPzgfZunj44MWLVpg8eLFiI6Olnrv7du3mDlzJu7evYtBgwZh3bp1nGar\n4OvrC5FIhL/++gtz5sxB27ZtoaqqClVVVbRt2xazZ8/Gnj17oKKigkOHDvGS0c/PD40bN8aBAwfg\n6uoq9ViOqqoqXFxcsH//fjRs2BAnTpzgJeObN2+grq4u9dq9e/dgYWGBpk2bSl5r27YtMjIyuI5X\nrYKCAmRkZCA9Pb3K//AhJiYGlpaW1T6C0bBhQyxduhQaGhp13hKd8IO6ULaE3IMAdaGsUBfKD3Wh\ncqE7nggh9Z4izIp4n6OjIxwdHSEWi5GVlQWgfDAo3zuSvIthGLi7u8Pd3R3p6elSwzb19fV5y6Wv\nrw8fHx+4ublh9uzZOHToEIyMjFBSUoLZs2fj+vXrcHBwwKZNm3gbXPro0SPY2Nh8cM6ClZUVbGxs\ncOfOHQ6T/ef58+fo1asX2rZtW+0xbdu2Rc+ePXHt2jUOk/1HU1MTKSkpkq/j4+ORl5dX6cpvWVlZ\nlY8WcSk3Nxe///47zp07h5cvX1Z7HMMwuH//PofJyuXl5UnNoVFVVQVQ/sGg4gONqqoqrK2tcf36\ndc7zkbqjLpQ9ofYgQF0oK9SFskVdqLzojidCSL3n5uaG0tJSTJs2Ddu2bUNSUpLkymlSUhK2b98O\nT09PiMVifP7553zHlaKiooLmzZujefPmglloV0VPTw9dunRBly5deF9sA4CJiQm2bt2KN2/e4Kuv\nvkJqairmzZuHy5cvo1evXti6dSuviy81NbVa7SCkq6uLRo0acZCosqZNm0JDQ6PG45o0aSJ1RZVL\nlpaWuHfvnuTRgb1794JhGPTq1UvquGfPnqFFixZ8RARQvtAeN24cDh48iFevXqFRo0ZgWRY6Ojpg\nWVbyn5YtW/KWU0dHB/n5+ZKvtbS0AJTvdvaut2/fIjc3l9NsRDaoC+VLaD0IUBfKAnWh7FAXKjc6\n8UQIqffGjRuHiRMnIi8vD97e3hg0aBCsrKxgZWWFQYMGYevWrcjNzcX48eN5nZtEZKtPnz5YtWoV\nUlJSMGzYMJw/fx5du3bF9u3bK+3mxDVra2vExsZWuT1vBZZlERsby/lA2gq9e/fGrVu3UFxcXO0x\nxcXFuH37dqXFLVc8PDwgFosxceJE9OzZE0FBQWjTpg3s7Owkx2RmZuLff//ldQDo7t278ezZM4we\nPRo3b97EkCFDwDAMrly5gps3b2Lp0qXQ0tJCnz59cOnSJV4ytmrVSuqKuZmZGViWRXBwsOS1169f\n48aNG7wNfSV1Q12onKgL64a6UHaoC5UbPWpHCFEKXl5e6NevH/bt24c7d+5IFhANGzZEt27d4O7u\nDicnJ85z1eX5cIZhMGvWLBmm+Xh37tzB1atXkZGRIdkC+X18bjfr7OyM1NRU/Pbbb7CwsMDu3bsr\nzUHgwzfffIMJEyZg7dq1mD9/vuRW7gqlpaXYsGED0tLSsGXLFl4yzps3D2PGjMHChQuxdOnSSlel\ns7OzsXz5crx9+xbffvstLxnt7Ozwyy+/YNu2bcjMzIStrS2WLVsmdUfEyZMnIRaLYWtry0tGAAgP\nD4eOjg6WL1+ORo0aSQ131tDQgJubGywtLTFx4kRYWVnxcrdJ7969sWPHDqSkpMDQ0BAODg7Q0tLC\nzp078fTpU+jr6+PcuXMoKCjg5d9KIhvUhbIn9B4EqAvrgrpQdqgLlRvDfugUMyGE1ENCmhVhamoK\nhmE+eLXvfRXHMwyD+Ph4OaarXnFxMebNm4cLFy4AwAfzyzunh4dHjcfcvn0bHTt2rHQbPMMw2Ldv\nn7yiVSswMBB3797F0aNHoaenh8GDB6N169YAym/nPnv2LNLS0jBx4kR06dKl0vc7OzvLPaO3tzeS\nk5MRGBgINTU19O3bVypjZGQkioqKMHr0aMnrFfj+IPiuoqIilJSUQF1dnbe/6127doWNjQ127doF\nAFi8eDECAwMRGxsrlWny5MkoLCyEn58f5xkfPXqEv/76C87OzujRowcAICwsDPPnz5caNG1ubg5f\nX19BfGgldUNdWDdC6kGAulBeqAtlh7pQudGJJ0II4VFdd8SYPXu2jJJ8nI0bN2LXrl1QV1fH6NGj\n0aFDhw/OQHBxcZFbFlNT00/+Xr4+sLz/Iev97c2re70CF5lr+0Hw/f8dfJ8UFaJu3bphwIAB2LRp\nEwBg5cqVOHToEC5fvozmzZtLjvv+++9x4cIF3L59m6+olaSnp+PChQvIyclBhw4d4OjoKNgZO0Rx\nKWIXCqkHAepCeaEulB3qQuVGj9oRQgiP+DpxVFenT5+GmpoaTpw4gQ4dOvCaZf/+/bz+/E/h7Oxc\n7UJaKGbNmiX4jIpCT08Pqampkq8r5kLExcXBwcFB8vqTJ094n7nyPj09PUycOJHvGKSeU8QuFFIP\nAtSF8kJdKDvUhcqNTjwRQuodRZ4VoSgyMjLQs2dPQSy2+ZxX8KnWrl3Ld4QazZkzh+8ItXbv3j2c\nPXsWT58+RX5+fpVXpvl6lAQovyU/KioKYrEYKioq6N27N1iWxaZNm9CuXTvo6+vj4MGDuH//Pnr2\n7MlLRlL/UBfKl5B6EKAulBfqQtmhLlRudOKJEFLveHt712lWBC22a6arq1ur7YUJkbfVq1fD19e3\n0iMOFd79u82Xfv364fTp04iMjISDgwPMzc3h4OCAS5cuYejQoVJZv/76a95yAsCNGzfg6+uLu3fv\nIjMzE6NGjZIMRY6KisL169fh7u7O65bcpHaoC+WLepAICXWhbFEXyh6deCKE1DuKeMt+VR49eoQn\nT54gPz+/2mO4GKxZlYqFQmlpKUQiYVVJamoqrl+/Disrq2qvRD9+/Bj37t1Dr169oK+vz3FCIit/\n//03Dhw4AAMDA3z99dcICQnBlStX8Oeff+LZs2c4deoU7ty5g6+++gr29va85Rw+fDhsbGygpaUl\neW3Tpk349ddfcfbsWeTm5qJ9+/aYNWsWr1d5t27diu3bt0t9WHn3vzdt2hS7du2Cnp4eJk2axEdE\n8hGoC+VLyD0IUBcqE+pC2aIulA8aLk4IIQJz+/ZtLF26FI8ePar2GL6HVr5+/Rqurq5wcHDAzz//\nLKhn8detW4e9e/ciODj4g4vtYcOG4auvvsL333/PcUIiKx4eHrh9+zZOnz6Ntm3bSnbIeffvhbe3\nN3bs2IHDhw+jc+fOPKYVtvDwcHz99dcwMDDAokWLYGNjgz59+sDFxQVr1qyRHNe3b1+YmZlh9+7d\nPKYlykDoXSjkHgSoC5UJdaHsUBfKj/BOzxNCiBJ79OgRvvjiCxQWFqJbt2549eoVkpOTMWzYMDx/\n/hzx8fEQi8VwcnKqtB0ylw4fPgw7OzscP34ckZGR6NmzJwwNDau8hZvrRzaioqJgbGz8wbkbHTp0\nQMeOHREZGUmLbQX24MEDdOnSBW3btq32mFmzZiEoKAg7duzAtm3bOEynWA4cOICGDRti9+7dMDIy\nqvY4U1NTPH/+nMNkRBkpQhcKuQcB6kJlQl0oO9SF8kMnngghREB27dqFwsJCrFixAuPHj8fixYuR\nnJyMjRs3AihfjP/www94+vQpjhw5wlvOd2eHpKSkICAgoNIxfM0KSUtLq9WQ1bZt2+LmzZscJCLy\nUlBQIPV4iKqqKgAgPz9fMnuFYRh07twZ165d4yXj+x4/fiyZGWFsbIz+/ftL3isrK0ODBg14yRUX\nF4cuXbp8cKENlM+1EdIW16R+UoQuFHIPAtSFyoS6UHaoC+WHTjwRQpSKUGdFVLhx4wbatWuH8ePH\nV/m+kZERdu7cic8++wzbt2/HwoULOU5YTsizQ96+fStZdH2IqqoqCgsLOUhE5KVZs2bIzs6WfK2r\nqwsAeP78OczNzSWv5+XloaCggPN870pLS8OPP/6Iq1evSl5zdnaWLLYPHz6MFStWYM+ePejduzfn\n+YqKiiS/fh+Sk5PDQRoib9SFdSfkHgSoC5UJdaHsUBfKD514IoQohY+ZFcHnYvvly5dSV30qrvgU\nFxdL5kc0a9YMtra2CAsLoxNPVdDT00NcXFyNx92/fx/NmzfnIBGRl7Zt2yI5OVnydefOncGyLI4c\nOYIVK1YAKL+qev36dbRr146vmMjKyoKbmxtSUlJgbGwMa2trHDt2TOqYoUOHYtWqVTh//jwvi+0W\nLVrg8ePHNR6XmJiIVq1acZCIyAN1oewIuQcB6kJlQl0oO9SF8sPPPWyEEMKhilkRiYmJ6Nq1K1q3\nbg0AGDZsGCwtLaGiogIAcHJy4nWhDQDq6upSX1fcIp2RkSH1eqNGjZCens5ZLkXSs2dPJCUlwd/f\nv9pjAgIC8Pz5c/Tq1YvDZETW+vbti+fPn0s+RNvb20NPTw/Hjx/H2LFjMWfOHEyYMAElJSUYPXo0\nbzl9fHyQkpICT09PnDx5UvJB4F3a2tro1KkTb7fu9+zZE4mJiYiMjKz2mNOnTyMlJQV9+vThMBmR\nFepC5UJdqDyoC2WHulB+6MQTIaTee3dWxOHDh9GjRw8AwMaNG3H8+HEEBQXBwsICT58+xU8//cRr\nVn19faSkpEi+rhgKev36dclrJSUluHfvXq1uBeZKRkYGYmJiEBMTU+mDAdemTp0KkUiEpUuXYvPm\nzUhKSpK8l5SUhM2bN2PJkiUQiUSYNm0aj0kV19OnT3H27FnExMTwmmPkyJH45ptvJI+JNGzYEFu2\nbIGuri5iY2MRGhqKvLw8ODg4YOrUqbzlDA8PR6tWrTB//vwPzq1o06YNb39/vvjiC4hEIsydOxdH\njx5FVlaW5L3CwkIEBgZi6dKlUFNTg7u7Oy8ZSd1QF8qXkHoQoC7kAnXhx6EuVG70qB0hpN5ThFkR\nFaytreHv7y8ZCNm/f3+oqKhgzZo1ePv2LfT19XHs2DGkpaVh2LBhvOWscOzYMfz555+VdvZo164d\nvvjiC4wbN47zTEZGRli5ciV+/vln+Pj4wMfHR3IlXywWAyh/bGPlypXo2LEj5/nelZ6ejmvXriEj\nIwNv376t8hg+htICwLlz53D8+HHMnj0bXbp0kby+fft2eHt7g2VZAMDw4cOxYcMGzvMBgKGhIWbO\nnCn1Wrdu3XD+/HlER0cjJycHHTp0kJpxwYfU1FT079+/xmGpqqqqUnM6uGRkZIS1a9di0aJF8PLy\nwvLly8EwDE6dOoXAwEAAgIqKCn799Ve0adOGl4ykbqgL5UOIPQhQF8oKdaHsUBcqNzrxRAip9xRh\nVkSFQYMGISoqCtevX8fAgQOhp6eH6dOnY/v27Vi5ciWA8vkbmpqa+O6773jLCQCLFi1CUFCQZB5I\ny5YtAZRf9X369CmWLl2K27dvY82aNZxnc3Z2hpGREf744w9cvXpVchVQTU0NvXv3xowZM2BlZcV5\nrgosy2L16tU4fPgwysrKJK+9i8/dkADg5MmTuHnzJjp16iR57d9//8Xvv/8OkUiELl26IDExEcHB\nwRg0aBAGDRrEecbqNG7cGPb29nzHkGjcuDHy8vJqPO7FixfQ1NTkIFHVhg8fDmNjY/zxxx+IjIxE\nfn4+SktL0bhxY/Tu3RuzZs2CpaUlb/lI3VAXyp6QexCgLpQF6kLZoS5UbnTiiRBS731oVkTFjAtA\nGLMievfujXPnzkm99s0338DExARnz55FTk4O2rdvjylTpvA61PDvv/9GYGAgmjVrhjlz5sDV1VXy\nwaW4uBj+/v7w9vZGYGAg7OzsMHz4cM4zdu7cGdu3b0dZWRmysrLAMAy0tbV526L3Xbt374avry8a\nNGgAe3t7dOjQQfLnUiju378PExMTqKmpSV47efIkGIbBqlWr4OzsjKSkJAwbNgzHjh0TxGK7uLgY\n2dnZaNiwIbS1tfmOI2FsbIz79+9LbW39vvT0dMTHx0sef+KLiYkJtmzZApZlkZWVhWY7TusAACAA\nSURBVLKyMujo6EjulCCKi7pQthShBwHqwrqiLpQd6kLlRieeCCH13odmRVQstoU2K+J9gwcPxuDB\ng/mOIXHs2DGoqqpi3759MDY2lnqvYcOGmDhxInr06AFnZ2ccPXqUtwU3UH5Vv1mzZrz9/Kr4+/tD\nJBJh7969vC+uqpOdnY3OnTtLvRYdHQ11dXWMGDECQPkchu7du9dqBxh5CgwMxIEDB5CQkICysjI4\nOztL7jAIDQ1FSEgI5s2bx9tt8cOHD8fKlSuxbNkyrF27ttIW5xVX/YuLizFy5EheMq5ZswZNmzaV\n7NTFMIxg/z0kn4a6ULYUqQcB6sJPRV0oO9SFyo1OPBFC6j1FmhXh4uKCNm3a4Pfff+c1R00SEhJg\na2tbabH9LmNjY/Ts2ZPXoZvFxcWIi4uTXL3X09ODhYWF5Ko0X5KTk9GjRw/BLrSB8l+7dx95KC4u\nRnx8PGxsbCAS/bd8aN68OW+7zwDSj7qoq6ujoKBA6v327dsjODgYZmZm+PLLL3nJOH78eJw6dQrB\nwcGIjY2VPO6UmJiIzZs3IzQ0FI8fP4aNjQ1vOw75+vrC0dGRl59NuEFdKFuK0oMAdWFdUBfKDnWh\ncqMTT4SQek9RZkUAwOPHj9G+fXteM9RGYWFhrW7f1tbWRlFREQeJpJWUlMDb2xsHDx7EmzdvpN5T\nV1fH5MmTMXv27EpX27iiqakpuCvP72vZsqVka2YAuHnzJoqLi2FtbS11XEFBAW+PRgQEBCAwMBBm\nZmZYtWoVzM3NYWZmJnWMsbExDAwMEBERwdtiW1VVFbt27cJPP/2Ec+fOYd++fQAg2QELAPr374/1\n69eDYRheMjZv3pweIajnqAtlS+g9CFAXygJ1oexQFyo3OvFECKn3FGFWRAVDQ8NKV6mESE9PD/fu\n3ZMM/KwKy7KIiYmRDFvlilgsxowZM3DlyhWwLIsWLVpIbitPSkrCy5cv4ePjg9jYWKldfrjUq1cv\n3q+A18TGxgYnT57Erl27YG9vj99++w0Mw1QaVPrw4UPo6+vzkvHYsWNo0qQJduzYAT09vWqP69Sp\nExITEzlMVlnTpk3x+++/499//8WlS5eQlJSEsrIy6Ovro1+/frwO+AWAPn36ICoqCqWlpVJX8Un9\nQV0oW0LuQYC6UFaoC2WLulCJsYQQQgRj06ZNrLW1Nfv69Wu+o3zQsmXLWFNTU3bt2rVsaWlppffF\nYjG7bt061tTUlPXy8uI028GDB1kTExN28ODBbERERKX3IyIi2CFDhrCmpqbsoUOHOM1W4dmzZ2z3\n7t1Zb29vXn5+bTx+/Ji1trZmTU1NWVNTU9bExISdNm1apWNMTEzYZcuW8ZLR2tqa9fT0lHrNxMSE\nXbRokdRr33//PWtlZcVlNCkFBQVsYWEhbz+/Nl68eMHa2tqyP/74I/vmzRu+4xAlpwhdKOQeZFnq\nQlmhLpQd6kLlRqfxCCH1niLMiqgwc+ZMXLlyBV9++SWWLVuGLl268B2pStOnT8fp06exd+9ehIaG\nYsSIEWjdujUYhkFSUhKCg4ORnJwMTU1NTJ8+ndNsQUFBUFNTw759+6q88mdvb4+9e/diyJAhCAwM\nxOeff85pPgC4ffs2XF1d4e3tjYiICNjb28PQ0LDaXYacnZ05Tlg+D+Lw4cP466+/kJmZic6dO1e6\nPf/q1aswNTXFgAEDOM8HAKWlpZV26qpKZmYmr1cuu3XrBktLS5w4cYK3DDXx9/eHvb09/P39ER4e\njj59+sDQ0BCNGjWqdCxf25qTuqEulC0h9yBAXSgr1IWyQ12o3BiWfWdaGiGE1ENdunTBwIEDsWnT\nJr6j1MjDwwPFxcW4e/cuGIZBs2bN0KpVq2oLr+L5eD7cvXsX8+bNQ1paWqXHDFiWhYGBAbZs2cL5\nB4bu3bvDxsYGO3bs+OBxM2bMQHR0NG7dusVRsv+YmpqCYRjJwNKaZhnEx8dzEUvhDBkyBCzL4uzZ\ns5LXTE1N4eLiItnJRywWo3///mjevDkCAgJ4ydm9e3cMGDAAGzZs4OXn18b7fyarUvE+wzD0Z1IB\nURfKnlB7EKAuVCbUhbJDXSg/dMcTIaTeU4RZERVu3Lgh+e8sy+LVq1d49epVlcfyNXixQteuXXHu\n3DmcOXMG0dHRUrvl2NjYYOjQobzsmFNaWgo1NbUaj1NTU0NpaSkHiSpzdnbm/fevPrCzs8PBgwcR\nFBRU7Q44R44cwcuXLzFmzBiO0/2nQ4cOkr8fQjVr1iz6M1nPURfKnlB7EKAuVCbUhbJDXSg/dMcT\nIaTe27x5M3x9fREaGgpdXV2+43zQu4vt2rC1tZVTEsU1dOhQ5Ofn4/z589Uu+IuLizFw4EBoaGjg\nzJkzHCdUPGKxGNnZ2Xj79m21xxgaGnKYqFxaWhpGjBiBoqIieHp6YvDgwRgzZgyGDx+Or7/+GmfO\nnMHOnTvRpEkTBAcH87Z70rFjx7B8+XKcOHGi0k5DhHCFulC5UBfKHnVh3VAXKjc68UQIqfeKiorg\n7u4OsVgs2FkRRHbWr1+PP//8E8OGDYOXlxc0NTWl3s/Ly8Py5csRHByML774AvPnz+cpqfD9888/\n+P333yXbR1eHYRjcv3+fw2T/uXbtGr755hvk5eVVeo9lWWhoaGD79u28fzD18vLCmTNn8L///Q+f\nffYZDAwMaMccwinqQuVCXSg71IWyQ12ovOjEEyGk3lOUWRGKSmhXALOysuDi4oL09HQ0adIEAwYM\nkBr4euHCBbx58wb6+voICAiAtrY2Z9mq8+zZM2RmZkJbWxvt27fnOw4A4NatW5g2bZpkka2lpYUm\nTZpUe3x4eDhX0Sp5+fIl9u7di4iICCQnJ0MsFsPAwAD29vb48ssvedviuoKlpSWA8r8rFRiGqXaA\nbmxsLCe5iHKhLpQfofUgQF0oK9SFskNdqNzoxBMhpN4zNTWt9bFCGRRYVlaGiIgI3LlzB1lZWbCy\nssLYsWMBlO9KkpOTg7Zt20JFRYW3jDdv3sS2bdtw69YtlJSUVHscH1cAnz17hu+//16yaKl4Xr+i\n8jp37oyNGzeibdu2nOZ6V2lpKXbs2IFDhw4hKysLQPm8i4pBoCdPnsShQ4ewYsUKdOrUifN8U6dO\nxbVr1zB+/HjMnTuXt1vz64OP+TcIABISEuSUpHre3t61Oq5hw4bQ0dGBhYUFzM3N5ZyKyBJ1oewJ\nuQcB6kJZoC6UHepC5Ub3tRFC6r39+/fzHeGjxMXF4bvvvsPz588lu2aUlJRIFttXrlzBggULsG3b\nNjg6OvKSMTIyEjNmzJAMJNXW1q7VVr5cadeuHU6cOIGbN29WOfC1R48evOYrLS3F9OnTcfXqVaio\nqMDIyAiJiYlSx1hbW2PhwoU4d+4cL4vte/fuwcjICCtWrOD8Z9fW7Nmz0bx5c3h5efEd5YPi4uL4\njlAjb2/vWg1Urfg3CQBMTEywZs0amtWhIKgLZUvoPQhQF8oCdaHsUBcqNzrxRAip9/h+nv1jvHjx\nAp6ensjJyUH//v1hY2OD9evXSx0zcOBAqKqqIiwsjLcTT7/99htKS0vh6emJ//3vf9DS0uIlR016\n9OjB+8K6Kr6+vrhy5Qr69OmDtWvXomXLlpWuBLZu3Rrt2rVDZGQkZs+ezXlGlmVhYmLC+c/9GBcv\nXoSTkxPfMWrE552JtTVr1iykpKQgICAAampq6Nu3LwwNDdGgQQO8ePECUVFRKCwshIuLC1RUVHDr\n1i0kJCRg6tSpCAgI4GWgLvk41IWypSg9CFAX1gV1oexQFyo3OvFECCECsmPHDuTk5GDJkiWYNGkS\nAFRabKupqcHU1BQxMTF8RAQA/Pvvv7CwsMDChQt5y6DITp48CW1tbWzZsqXSwNd3dejQgbfHXUxM\nTPDy5UtefnZt6enp8bYNeH0zduxYuLq6Yvjw4fj555+ho6Mj9X5OTg5WrlyJixcvws/PD3p6eli5\nciWOHDmCPXv24Oeff+YpOamPFKELqQfrjrpQNqgLZYe6UH7oxBMhRGkIfVYEUH7rvpGRkWShXZ1W\nrVrh6tWrHKWqTENDA//3f//H28+vrbS0NGRkZHxw4KuNjQ2Hico9efIEtra2H1xoA0CTJk2QmZnJ\nUSppHh4emD9/PuLj4wV7+/iAAQPw999/o7CwEGpqanzHUWhbtmxBo0aNsHbtWqiqqlZ6X0tLC2vW\nrMFnn32GLVu24Ndff8UPP/yAs2fPIjIykofE5FNRF8qGovQgQF1YF9SFyoW6UH7oxBMhRCkIfVZE\nhVevXmHgwIE1HseyLN68ecNBoqr16NED//77L28/vybnzp3Dxo0b8fz58w8ex+fWx7WZIZCRkVHl\njlNcGDZsGBITEzFt2jR888036N+/v+BuIZ89ezYuXryIb775BitWrICBgQHfkapUsZNPTVRVVaGj\nowNLS0u4uLhgwIABck72n6ioKNja2la50H43X7du3XDlyhUA/91xcufOHa5ikjqiLpQdofcgQF0o\nC9SFskNdqNzoxBMhpN5ThFkRFZo0aYLXr1/XeFxSUlKl23+5NHv2bEyYMAF79+7F1KlTectRlfDw\ncMybNw9lZWVo2rQp2rRp88Gtj/nQunVrPHjwAGVlZdVuI1xUVIQHDx7AyMiI43Tl3r2yu3LlSqxc\nubLaY/n60LJu3ToYGxvj4sWLGDRoECwsLGBoaFjt9vC//PIL5xkB1PoRiNLSUhQWFiIlJQWhoaFw\ndXXF6tWr5ZyuXG5uLgoKCmo8rrCwELm5uZKvdXV15RmLyBB1oWwJuQcB6kJZoS6UHepC5UYnnggh\n9Z4izIqoYG5ujjt37iAjIwMtW7as8pjHjx8jISGB0ytA7+vYsSP27NmD7777DmfPnoW9vT309fWr\nXTg6Oztzlm3nzp1gWRbz5s3DF1988cGrVnxxdHSEj48P9uzZgy+//LLKY3bv3o3c3FzePvxVbLct\n62NlKSAgQHK1vKSkBHfv3sXdu3erPJbPxXZcXBw2btyIw4cPY+LEiRgxYoTUsNJTp07h6NGjmDBh\nAiZPnoxr165h48aN8Pf3R58+fTB8+HC5Z2zdujWuX7+OlJSUaq/mp6Sk4Nq1a2jdurXktYyMDGhr\na8s9H6k76kLZEnIPAtSFskJdKDvUhcqNTjwRQuo9RZgVUWHMmDG4cuUK5s+fj99++63Sldz8/Hws\nXboUZWVlkkcj+HLz5k1kZ2cjNTW12gVOBS4X3A8ePICZmRlmzJjB2c/8WNOmTYO/vz82btyI+Ph4\nDB48GACQlZWFS5cuISQkBIGBgTAwMICbmxsvGRMSEnj5uR9jzZo1fEeolZMnT2Lfvn04cOAArK2t\npd7T0tKCubk5PvvsM3h4eMDY2BhjxoxB+/bt4ebmBn9/f04W287Ozti0aRM8PDwwb948DB06VDLj\nRywWIyQkBJs3b8bbt28lf59LS0vx4MEDdOnSRe75SN1RF8qeUHsQoC6UFepC2aEuVG504okQUu8p\nwqyICsOHD0dISAhCQ0Ph5OQkGfb5zz//YN68ebh69SpycnIwbNgw9O/fn7ecR44cwYYNGwAApqam\naNu2rWBu4ReJRGjfvj3fMT5IW1sbu3fvxtdff43g4GCcPn0aDMPg0qVLuHTpEliWhYGBAXbs2AEN\nDQ2+4wqWi4sL3xFqxdfXF927d6+00H6XtbU1rK2tcfDgQYwZMwbW1tYwMzPj7LENT09P3LhxA5GR\nkViwYAEWLVqEFi1agGEYZGRkQCwWg2VZ2NnZwdPTEwCQmJgIY2NjjBgxgpOMpG6oC2VLyD0IUBcq\nE+pC2aEulB868UQIqfcUYVbEuzZv3owtW7bA19cXFy9eBFD+SMHjx48hEokwZcoULFiwgNeM+/fv\nh0gkwvbt29GvXz9es7zPwsICycnJfMeokYmJCU6fPg0/Pz9EREQgOTkZYrEYBgYG6NevH8aPHw91\ndXW+YxIZePz4ca0eE2nRooXUI05t2rTBw4cP5RlNQiQSwcfHB/v378eBAwfw4sULpKamSt43NDSE\nu7s7PDw8JFd/TU1NcejQIU7ykbqjLpQtIfcgQF1IhIe6ULnRiSdCSL2nCLMi3iUSiTB//nx89dVX\nuH79OpKSkiSLsD59+qBZs2Z8R8SLFy9gY2MjyMX29OnT8eWXXyIqKgp9+/blO84HNWrUCG5ubrw9\nQlAbz549w5EjR3D37l1kZmZi4MCBWLhwIYDyuw8SEhIwdOjQGrfDVmaqqqp48OBBjcc9ePBAag5L\naWkpp3dQNGjQAFOnTsXUqVORlpaG9PR0AEDLli0Fu0sSqT3qQtkScg8C1IWyRl1Yd9SFyo1OPBFC\n6j1FmRXxPi0tLQwaNIjvGFXS1dUVzBDFlJQUqa/bt2+PGTNmYObMmXB3d0f//v1hYGBQ7cBXoW2L\nLCTHjx/HihUrUFJSAqB8KGlWVpbk/cLCQnh5eUEkEmHMmDGc51u8eHGtjnt3a2YHBwc0bNhQzsmk\nWVtb49KlS9ixY0e181Z27tyJxMREqQ/8ycnJaNGiBVcxpejr60NfX5+Xn03kg7pQtoTUgwB1oTxR\nF8oGdaFyY1i+xu8TQgiH5syZg9DQUDRp0gQ2Nja4ePEiOnTogE6dOknNiti0aRPfURXCqlWrcO7c\nOYSFhXG+cHmfqampZDeXd7EsW+Xr7+Jr62NFcOvWLbi7u0NdXR1ff/01evTogfHjx8PFxUUyyLSs\nrAy9evWCjY0Ntm3bxnlGU1NTAJD8Pr+/pHn/dYZh0KxZM6xZswb29vac5YyLi8Pnn3+OkpISGBsb\nY+jQoTA0NATDMEhJScHp06eRmJgIVVVVHDlyBObm5khNTcWAAQPw+eefY9myZZxlJfUbdaHsCKkH\nAepCeaEulB3qQuVGJ54IIUqhtLRUMiuiqKhI6j2RSIRJkyZhwYIFEImEcSPo27dvERMTg4yMDBQX\nF1d7HNe75FTIzc3FhAkTYGpqimXLlvF61beuWyyHh4fLKEn1PDw8Pvl7GYbBvn37ZJimdmbOnImI\niAj4+vqiW7duAMoXt+8utgFgypQpSE9PR0hICOcZAwICEBsbi4MHD0JfXx+DBw+W2pr53LlzSElJ\ngZubG1q0aIHr16/j2rVraNSoEU6cOIGOHTtyljUqKgoLFy7E69evK30IZFkWurq6WLduneRDwOvX\nr3H//n107NiRrrYSmaEulB0h9SBAXSgv1IWyRV2ovOjEEyFEqeTk5AhyVsS7fHx84OPjU6tdheLj\n4zlIVNnixYuRl5eH8+fPo0mTJrC0tIS+vn6VV1UZhsEvv/zCQ0rhqLgS/SmVyzAML7/PvXv3Rrt2\n7XDkyBHJa1Uttr///ntcvHgRt27d4jzjv//+iwkTJsDNzQ3ffvttpQ/LpaWl2Lx5Mw4ePIijR4/C\nxMQE27dvx++//17pfwcXCgoKcObMGURHR0vNjOjRoweGDRsmqB2xSP1GXVh31IMfj7pQPqgLiSIQ\nxuUMQgjhiFBnRVTYu3ev5BEHExMTtGvXTpAFHBAQIFk85ufn49q1a9UeSwvu/1hZWWH06NFo3rw5\n31FqlJeXV6uriwUFBRCLxRwkqmzr1q1o2bJltTtbVQwnDgsLw9atW+Ht7Y3p06fjyJEjuHHjBsdp\nAXV1dYwZM4aXGSCEvIu6sO6oBz8ddaFsURcSRUAnngghREAOHz4MkUiEbdu2wcHBge841eL66pii\nGzFiBMLCwnDv3j3cv38fdnZ2cHV1haOjo2AeaXlfs2bNarUV95MnT6Cnp8dBospu3ryJPn36fPAY\nhmFgaWmJqKgoAOUL8E6dOvGy2CaE1I4idCH14MejLpQP6kKiCIT5N5wQQuRAyLMiKqSkpMDW1law\nC+0KLi4ufEdQKBs2bEB+fj6Cg4MREBCAixcv4tKlS9DS0sLIkSPh6uoKMzMzvmNKsba2xtmzZxET\nE4POnTtXeUxUVBSePn2KcePGcZyuXEFBgdTOQtXJyspCYWGh5GtNTU2oqKjIMxohgkVdKBvUgx+P\nulA+qAuJIqATT4QQpfAxsyL4XGy3aNECmpqavP18Ij8aGhqYMGECJkyYgCdPnsDf3x9BQUE4cOAA\nfH19YWJiAldXV4wYMQK6urp8x8XUqVMREhKCOXPmYNWqVZWupkZHR+PHH3+ESCTC5MmTecnYvn17\n3LhxAwkJCZJdfd6XkJCAGzduSA1PTU9Pr7SVPCHKgLqQ8I26UPaoC4kioOHihJB6b+/evVi7di2A\n2s2K4PP2+dWrVyMkJATnz58XxPbMRL7Kyspw+fJlBAQEIDw8HCUlJVBRUcHQoUOxfv16vuNhz549\n+PXXX8EwDDQ0NJCfn4+mTZtCJBIhKysLLMti0aJFmDp1Ki/5jh49imXLlkFLSwuenp4YPnw4DAwM\nwDAMUlNTERwcjD///BO5ubnw8vLChAkTUFRUhN69e6Nv377w9vbmJbfQ5eXlISYmBpmZmTA0NIS1\ntTXfkYgMUBcSoaIurBvqQvmgLpQtOvFECKn3Bg8ejBcvXgh6VkSF3NxcjBs3Dubm5oLYnrkm6enp\nOH/+PJ4+fYr8/Pwqd6qhoaq1k5WVhcWLF+PixYvQ0dHB1atX+Y4EALh06RK8vb0RExMj9XqnTp0w\nd+5cDBw4kKdk5ZYsWYLjx49LdpJq0KABgPIPMkD59sxjx47FqlWrAACJiYnYuXMnRowYIfh/D7iW\nl5eHX375BadOnZIMyXV2dpacgDh+/Dh+++03eHt7o2vXrnxGJZ+AulA+qAdli7rw01AXyg51oXzQ\niSdCSL3XuXNn2NjYYM+ePXxHqZWcnBxMnjwZqampgt6eee/evdi4cSNKS0slr1VUSkVelmV52wJZ\nUTx+/BgBAQEICgrCy5cvwbIsunfvjoMHD/IdTUpWVhaSk5NRVlYGfX193oaoViUsLAz79+/H3bt3\nJTNrVFVV0bVrV7i7uwt69y6hKCgogJubGxISEtCsWTNYWlri0qVLUlttv3z5Ev369cMXX3yB+fPn\n85yYfCzqQtmjHpQd6sK6oy6sO+pC+aEZT4SQek+RZkUUFRVhwYIFSExMBMuygt2e+fLly1i7di00\nNDTg6emJGzdu4O7du1ixYgWePXuGc+fOITk5GR4eHoIbFCoE+fn5+PvvvxEQEIB79+6BZVloa2tj\n0qRJcHV1hbm5Od8RK9HR0RHsLAgnJyc4OTlBLBZLBqxqa2sLdpckIdqzZw8SEhIwatQoLF++HGpq\napVmhbRo0QLGxsYf/HeJCBd1oWxRD9YddaFsURfWHXWh/NCfQkJIvTdw4ECEhISguLhY8LMitmzZ\ngoiICGhra2PUqFFo164d1NXV+Y5VyYEDB8AwDPbs2QMrKyssXrwYd+/exfjx4wEAc+fOxcqVK3Hi\nxAn4+/tzmi0iIgL9+vXj9GfWBsuyiIqKQkBAAM6fP4+ioiKoqKjAwcEBLi4ucHR0hKqqKt8xFZqK\nigqaN2/OdwyFFBISgpYtW2LVqlUf/Hfy//7v/3D37l0OkxFZoS6ULSH3IEBdqMyoCz8ddaH80Ikn\nQki9N2fOHEREROCHH34Q/KyIM2fOQEtLC0FBQYK6fft9MTExsLS0hJWVVZXvN2zYEEuXLpXMRNiw\nYQNn2aZPn4527drh888/h6urqyCu8G/atAlBQUHIyMgAy7IwMjKCq6srRo8eLZjFYWBgIIDyK6Ya\nGhqSr2uLzx2wFEVSUhKOHTuGu3fvIjMzE46Ojvj+++8BAPfu3cO///6LIUOGQENDg5dsdnZ2NZ6Q\naNiwIbKzszlKRWSJulC2hNyDAHXhp6IulD/qQuVEJ54IIfWepqYmjh07hsmTJ8PJyUmwsyIAIDs7\nG3Z2doJdaFfIy8tDmzZtJF9XXJ0sKCiQXJVWVVWFtbU1rl+/zmk2c3Nz3L9/H+vWrcOWLVswfPhw\nuLm5wcLCgtMc7/Lx8QHDMLC0tISLiwu6dOkCoHwobXp6eo3fz0X2RYsWgWEYdOnSBRoaGpKva4sW\n2x8WEBAALy8vFBcXS2a+vPuB9c2bN1iyZAkaNGgAV1dXzvOJRCK8ffu2xuPS0tIEd+cJqR3qQtkS\ncg8C1IWfirpQvqgLlRedeCKE1HuKMCuiQps2bSQ7aAiZjo4O8vPzJV9raWkBAF68eIGOHTtKXn/7\n9i1yc3M5zebv749//vkHBw8eREhICPz8/ODv7w8rKyu4ublh6NChvD1mEhsbi9jY2I/6HoZhcP/+\nfTkl+o+zszMYhkHTpk2lviZ1d+fOHfz0009o3Lgxvv32W9jY2ODzzz+XOqZnz55o2rQpwsPDeVls\nt2/fHvHx8R98DCsnJwcJCQmCnLtCakZdKFtC7kGAuvBTURfKD3WhcqMTT4SQek8RZkVUGDNmDLZu\n3YpXr14J5rbzqrRq1QopKSmSr83MzMCyLIKDgzFv3jwAwOvXr3Hjxg0YGhpynq9Lly7o0qULFi9e\njOPHj+PIkSP4559/cO/ePaxduxZjx47FxIkT0apVK07y8PFr8LHWrl37wa/Jp9u1a5fk/3bv3r3K\nYxo0aABTU1MkJiZyGU1i8ODB2LhxI9avX4+ffvqpymM2bdqEgoICDB06lON0RBaoC2VL6D0IUBd+\nCupC+aEuVG504okQUu8pwqyIClOnTkVMTAw8PDywZMkS9OrVS5BX2nr37o0dO3YgJSUFhoaGcHBw\ngJaWFnbu3ImnT59CX18f586dQ0FBAZycnHjLqaOjg+nTp+Orr77CxYsXcejQIURFRWH37t3Ys2cP\n+vXrh0mTJsHOzk6uOcLDw+X6/58I2507d2BlZVXtQrtCixYtEBcXx1EqaZMnT0ZgYCB8fX0RGxsr\n2Xb7xYsXOHToEEJCQhAdHY1OnTph7NixvGQkdUNdKFuK0oMAdSERBupC5UYnnggh9Z4izIqoULE4\nTUlJgaenJ0QiEVq0aFHtDI6wsDCuIwIARowYgZcvX0oW3E2aNMHq1asxf/580kfZRgAAIABJREFU\nhISESI4zNzfHzJkzecn4LoZhMGDAAPTs2RPbtm3Dn3/+CbFYjAsXLuDixYswNjbGggULBLkDEFF8\n+fn5MDAwqPG4goIC3h4vUlNTw549ezB37lzcuXNHsltPdHQ0oqOjwbIsLCwssH37dsHviEaqRl0o\nW4rWgwB1IeEXdaFyoxNPhJB6TxFmRVR48eKF1NclJSVSt/K/i8+rv0ZGRli1apXUa05OTjh79iwu\nXLiAnJwcdOjQAY6OjlBRUeEp5X8ePXqEw4cPIygoCPn5+WAYBn369EHfvn1x8uRJJCQk4H//+x/W\nrVuHUaNG8R2XF9X9OastRXiEgi86OjpITk6u8binT5+iZcuWHCSqmp6eHo4cOYKIiAhEREQgKSkJ\nYrEYBgYG6NevH5ycnAR31wmpPepC2VK0HgSoC2uDulB+qAuVG514IoTUe4owK6LC+fPn+Y5QJ3p6\nepg4cSLfMQAAYrEYoaGhOHTokOQqlbq6Otzc3DBp0iR06NABAODp6YnQ0FB8//338PHxUdrFtqOj\n4ycvpLga+vohDx8+lGzNbGxsjIEDBwIAysrKUFpayuuVSWtra5w7dw5xcXHV7sp09epVPHnyBGPG\njOE4XWX9+vWjOx7qIepCbgipBwHqwo9FXSg/1IXKjU48EULqPUWYFVGBqwGf9Vl6ejqOHTuG48eP\n4+XLl2BZFu3atcPkyZPh4uICDQ2NSt/z2WefwcHBARcuXOAhsTAo6lXalJQULFq0CNHR0ZLXnJ2d\nJYvt48ePw8vLC3v27EHv3r15yThlyhScPXsWc+bMwerVq9GrVy+p92/fvo0ff/wRKioqcHd35yUj\nqf+oC5ULdeGnoS6UH+pC5UYnnggh9Z6QZ0VER0ejefPmaN++/Ud935UrV5CYmAgPDw85JVNcAwcO\nlDxOYm9vj8mTJ9fqipWmpiZKS0vlHU+wFHHoa2ZmJiZPnoyUlBR06tQJPXr0wKFDh6SOGTJkCFas\nWIHz58/zttju1q0bvv32W2zatAmenp7Q1NQEwzAIDw+Hvb09Xr16BZZlsXDhQpiamvKSkdR/1IXK\nhbrw01AXyg91oXKjE0+EkHpPyLMi3N3d4erqil9++aXSe7a2thg5ciSWLFlS6b1Tp04hMDCQs8W2\nmZkZGIZBcHAw2rdvDzMzs1p/L9e3njdq1AhjxozBpEmT0K5du1p/34IFC/D111/LMRmRNR8fH6Sk\npOCrr77Cd999B4ZhKi22tbS0YGJiglu3bvGUstz06dPRsWNHbN26VfL3IScnB0D5rJi5c+dKds/h\nQsVV8E/B58YG5NNRF9aNIvUgQF2oTKgLPx11IXfoxBMhpN4T+qwIlmWrfD03NxcFBQUcp6kay7JS\nOavLXN33cuny5ctQV1f/6O/T1taGtra2HBLVXyzL4tKlS/Dz88PWrVs5//kXLlxA69atJQvt6rRu\n3Zr3xTYADBgwAAMGDMCrV6+QnJwsGVbKx6Md75+E+BhCfTyLfBh1Yd0oUg8C1IVcoi78ONSFyolO\nPBFC6j2aFVF3CQkJH/xaSD5loU0+ztOnT+Hn54egoCC8fPmStxypqano379/jYs/kUgkuaIqBM2b\nN+d9uLPQT0IQ2aMurBtF6kGAupAL1IV1Q12oXOjEEyGEkHotLy8P+fn51V5xVtRBolwrLCzE6dOn\n4efnhzt37gAov8qrq6uLYcOG8ZKpcePGyMvLq/G4Fy9eQFNTk4NEioNOQhCiXKgLZYO6sH6hLuQO\nnXgihNQ7NKRU/l6/fo3r16/j4cOHyM7ORoMGDaClpYVOnTrB1tYWurq6vObLzs7Gb7/9hnPnziEz\nM7Pa44Sw9bHQ3bp1C35+fggJCUFhYSFYlgXDMBgyZAhGjx4Ne3t7qKio8JKtY8eOiIuLQ15eHpo2\nbVrlMenp6UhISICNjQ1nuU6dOlWn7x85cqSMkhBlRl0oX0LvQYC6UJaoCz8edSF5F514IoTUO4ow\npFRR5eTkYO3atTh16pRkt5z3iUQiuLi4YMGCBdUugOQpJycH48ePR1JSElRUVNC4cWMUFhaiRYsW\nkh1TGIaBgYEB59kUxcuXLxEQEAB/f388e/ZMcoXc1NQUr1+/xqtXr7B582aeUwIjRozA8uXLsXTp\nUqxbtw4NGzaUer+srAyrVq1CcXExRo0axVmuBQsW1Gn2Ay22iSxQF8qHIvQgQF0oC9SFdUNdSN5F\nJ54IIfWS0IeUKqJXr17B3d0dT58+Bcuy0NLSgoWFBXR0dFBWVoasrCzEx8cjJycHx48fx+3bt7F/\n/37Or/ru2rULz58/x5gxY7BkyRJ4eXkhKCgIly9fRmFhIU6dOoVNmzahe/fuWL9+PafZhEwsFiM8\nPBx+fn6IjIyEWCyW/D6PHDkSY8aMgZmZGdzc3PDq1Su+4wIAxo0bh1OnTuHMmTOIiYlB//79AQAP\nHz7E+vXrERYWhmfPnkk+ZHNlxIgRgh86WpeTCgzDYN++fTJMQ+SFulC2FKUHAerCT0VdKDvUheRd\ndOKJEEJ49urVK0RHR3/Ue3wMsVyyZAmePHmCdu3a4ccff4SDg0OVx124cAFr1qzBo0eP4OXlhd9/\n/53TnBcuXICuri6WLVuGhg0bSi161NTUMH78eJiZmWHChAno2rUrJk2axGk+obK3t0dWVhZYloWK\nigrs7e3h6uoKR0fHSldPhUIkEsHHxwdLlizBmTNn4OvrCwCIjY1FbGwsAMDJyQlr167ldPG7YcMG\nzn7Wp7px40aVr1f8Or1/wuLd14X+QYIoJkXoQkXpwYoM1IUfj7pQdqgLybvoxBMhhPAsMjISkZGR\nlV5nGKba97j24MEDXLhwAW3btsWJEyc++OjAgAEDYG1tjbFjxyI0NBSJiYkwNjbmLOuLFy9ga2tb\naYEoFosl8xc6d+6M7t27w8/Pjxbb/19mZiYYhoG+vj42bdoEa2trviPVioaGBjZv3ozZs2cjIiIC\nSUlJkq2Z+/XrB3Nzc74jCtL+/fsrvRYWFob9+/fDwsICo0aNkgxdffHiBU6ePIm4uDh4eHjAycmJ\n67hECQi9CxWpBwHqwk9FXahcqAu5QyeeCCGER4qyi0xwcDAYhsGiRYtqNa9CS0sLixYtwqxZs/D3\n339j3rx5HKQs16BBA2hoaEi+rthSOisrS2rb3pYtW+LChQuc5RI6fX19pKWlIS0tDZMnT0bPnj3h\n4uKCwYMHo1GjRnzHq5GRkRGMjIz4jqEwbG1tpb6Ojo7GwYMHsXDhQnh6elY6fsqUKdi7dy/Wr19P\ni20ic4rQhYrUgwB14aeiLlQu1IXcoRNPhBDCo/DwcL4j1EpMTAyaNm0KR0fHWn+Po6MjNDU1ce/e\nPTkmq6xly5ZITU2VfF1xpSouLk7qsYhHjx4J9rZ5Ply4cAFRUVE4ceIEwsPDcfXqVVy7dg0rVqzA\nsGHD4Orqiq5du/IdUyHcvn27Tt/PxxX2P/74A0ZGRlUutCtMnToV/v7+2LFjR6XFOiF1oQhdqEg9\nCFAXfirqQtmhLiTvohNPhBBCavTkyROYmZl91PcwDANzc3M8efJETqmqZmFhIRkIqqKigt69e4Nl\nWWzYsAGtW7eGnp4eDh06hISEBPTq1YvTbELGMAzs7OxgZ2eHnJwcnDx5En5+fkhISMCxY8dw/Phx\ntGvXDnl5eXxHFTw3N7dPnv3A17bmsbGx6NevX43HderUCZcuXeIgESHCokg9CFAXfirqQtmhLiTv\nohNPhJB6SRGGlCqS3NzcT9qVR1dXFzExMXJIVD17e3sEBwfj8uXL6N+/P8zMzDBgwABcuHABI0aM\nkBzHMAxmzZrFaTZFoaWlBXd3d7i7uyM+Ph4nTpzA33//jadPnwIo/7Xz9PTEqFGjMGjQIMkjHPL2\nsR/63sXlIrZbt26VFtulpaWSux7U1dWlZkYUFBSAYRh07twZIhE/S7OSkhKkpKTUeFxKSgpKS0s5\nSERkgbpQdhSpBwHqQlmgLqwb6kLyLoatbp9VQghRUKampnXaaSI+Pl6GaeoHMzMzODs7Y82aNR/1\nfYsXL0ZQUBCnV61KS0vx+vVraGhooEmTJgCAgoICbNy4EWfPnkV2djY6dOiAWbNmYfDgwZzlUnTF\nxcUICwuDn58frl69irKyMjAMg8aNG8PJyYmT7bhNTU3r9P0JCQkySvJx3r59i2nTpuH169dYsGBB\npbkQYWFh2LBhA3R1dfHXX3/xMkdk4sSJ+Oeff7Bjx45qd+q6dOkSZsyYga5du+Lw4cMcJyQfi7pQ\nthSpBwHqQnmhLvx01IXKjU48EULqnY+Zv1AVRZg1wTVTU1O4uLh80oI7MDCQPsDUM2lpafDz80Ng\nYCCSkpLAMAz9Hn/A5s2bsX//foSEhEBPT6/KY9LS0jB06FB4eHjg22+/5Thh+YJ/9uzZEIlEGDVq\nFEaOHInWrVsDKL8SferUKQQFBUEsFmPr1q00VFUBUBfKFvUgeR914cehLlRudOKJEEJIjUxNTWFv\nb4/p06d/1Pft3LkTUVFRtBCrx65duwZ/f3/8+uuvfEcRrEGDBsHIyAh//PHHB4+bOXMmEhMTERoa\nylEyabt27cKWLVtQVlZW6T2WZdGgQQPMmzfvo/8dIKQ+oB4kH0JdWDPqQuVGM54IIYTUSmRkJCIj\nI/mOQQSmV69eNJi2BmlpaTA3N6/xuEaNGiE9PZ2DRFX76quvYGdnhwMHDuDmzZtIS0sDAOjp6cHG\nxgaTJk2ChYUFb/kI4Rv1IKkOdWHNqAuVG514IoQQUiNDQ0O+I1TL29v7k7+XhqoSLujo6ODmzZso\nKipC48aNqzymqKgI0dHR0NbW5jidNDMzM/zyyy+8ZiBEiITcgwB1IRE+6kLlRo/aEUIIUWgVA3Q/\nps4qjqd5DIpl4MCBn/y9DMMgLCxMhmlqz8vLC0eOHEG/fv2wfPlyGBgYSL2fmpoKLy8vREREYOLE\niVi2bBkvOQkhiou6UHlQFxJFRCeeCCGEKLS6XOUFgNmzZ8soCZG36nby+dCHLSF8sMrMzMTYsWOR\nkpICkUgEa2trqWGlt279v/buPLiqKsHj+O8mAcJqDDEbhCXQEBACRJIQBEKjjpMRBMIqTNPS3QIz\nqChDT2kPpTKOynQzSiGIMERlE5WGsCowBNm3EHaTsO8JYQnEBCTbe/OHldekSVjk3XeT976fKqp4\nNyeVX5dN/U7OvfecNJWUlCg0NFR//etff9GR7QA8G13oOehCVEcsPAEAgGrhwoULd1ybP3++5s2b\np6efflrPP/+8YxJ7/vx5rVixQuvXr9dvf/tb/fM//7MaNWrk6sgOly5d0ttvv62NGzdW+ItBz549\nNWnSpEpP+nGFgoICLVy4UDt27NClS5dUWFhY4Tgr75gDgKejC81FF5qDhScAAFAtrV+/Xq+88oo+\n/PBDJSQkVDhmzZo1ev311zVt2jQ988wzLk54p3PnzlW4WWlYWJilubKzszV8+HBlZ2ff81UdXssB\ngKqDLnQeutA8LDwBANyazWbT9evXJUl+fn7y8vKyOBGc5YUXXpDNZtPXX39913FDhgyRYRj66quv\nXJSs+vnjH/+olStXqm3btnrppZcUHh6uevXqVTreyjvmAB4cXei+6ELnoQvNw6l2AAC3tHnzZs2d\nO1dpaWmOx6Rr1aqlJ554QiNGjFB8fLzFCfGwjhw5ol69et1zXOPGjbVx40bzA1Vj27ZtU0BAgObN\nm3fXSTaA6oUudH90ofPQheZh4QkA4Hbee+89LViwwPGYdNmd3Vu3bmnbtm3avn27hg8frokTJ1oZ\nEw/JMAydPHnynuNOnTrlgjT3VlRUpPT09LvuGSFJffr0cWGqnxUUFCg+Pp6JNuBG6ELPQBc6D11o\nHhaeAABuZenSpZo/f77q1q2rF198UX379nUc2Zudna0VK1boiy++0MKFC9WmTRsNGDDA4sT4pSIj\nI7Vz50598803Gjx4cIVjFi9erPT0dHXt2tXF6cqbP3++pk+frh9//PGeY62YbDdq1EjFxcUu/7kA\nzEEXeg660HnoQvOwxxMAwK0kJibq6NGj+vLLLxUZGVnhmIMHD2rYsGFq3bq1lixZ4uKEcJY9e/Zo\nxIgRstvtiomJUZ8+fcodzbxy5Urt2rVLXl5e+uKLLxQdHW1JzuTkZL355puSpKZNm95zz4i//OUv\nrormMHPmTM2ZM0fr16/Xo48+6vKfD8C56ELPQRc6D11oHhaeAABupUOHDoqKitLnn39+13EjR47U\nvn37tH//fhclgxlWrVqlt956Szdv3pRhGOW+ZrfbVbt2bU2aNEnPP/+8RQmlfv366ciRI/rggw/U\nr18/y3LcTUlJiV566SUVFBTogw8+UMuWLa2OBOAh0IWehS50DrrQPLxqBwBwK7Vr15a/v/89x/n7\n+6tWrVouSAQz9e7dWzExMVq8eHGFRzMPHDhQQUFBlmY8deqUOnbsWGUn2pL0u9/9TiUlJTp06JCe\nf/55hYSEKDQ09I5fYKSf9xOZO3euBSkB3C+60LPQhc5BF5qHhScAgFuJiorS4cOHZbfbK5woSD/f\n/Tt8+LCioqJcnA5mCAwM1NixY62OUSlfX1+FhoZaHeOudu/e7fi7zWbThQsXdOHChQrHVvbvCkDV\nQRd6Hrrw4dGF5mHhCQDgVl599VUNGTJEkydP1oQJE1SjRo1yXy8pKdGUKVN08eJFTZ061aKU8CSd\nOnXSiRMnrI5xV/PmzbM6AgAnogtR1dCFno09ngAAbmXZsmXav3+/vv76awUFBenZZ58tt8nm2rVr\ndfHiRQ0dOlQdOnS44/ur8iPgqJ7KNvB9//33Ld1fA4DnoAtR1dCFno2FJwCAW4mIiJBhGCqrt4o2\n2azoepmMjAxzA8Kpzp8/r9mzZ2vHjh26dOmSioqKKhxnGIbS09NdnO5ne/fu1caNGzVnzhwlJCSo\nZ8+eCgkJkZeXV4Xjee0FwMOiCz0LXYiqjlftAABupV+/frx37yGOHTumYcOGqaCgQPe6j2blfbZh\nw4Y5fgH89ttv9e2331Y61spfCgC4D7rQc9CFqA5YeAIAuJXJkydbHQEu8tFHHyk/P1/x8fEaO3as\nwsPDVa9ePatj3aFTp07V5hfAgwcPau3atTp9+nSlv8Rwkg9Q9dGFnoMudD660Pl41Q4AAFRLMTEx\nql+/vtasWXPHxrl4cO+9954WLFhQ7hWc26eJZZ8Nw+A1HACoIuhC56ILzVHxC5UAAABVXFFRkdq3\nb89E2wlWrVql+fPnKzg4WO+++66efPJJSVJSUpLeeustderUSXa7XS+99BJ3eAGgCqELnYcuNA8L\nTwAAoFpq1qyZ8vPzrY7hFr755hv5+Pho7ty5GjRokAIDAyVJTz75pIYNG6ZFixbp5Zdf1ueff67a\ntWtbnBYAUIYudB660Dzs8QQAAKqlQYMG6c9//rPOnz/vOCa8qissLNTZs2fvugmsFSf5HDlyRB06\ndFCTJk0qHTN27FgtX75cn376qWbMmOHCdACAytCFzkMXmoeFJwAAUC0NHz5chw4d0siRIzVx4kR1\n79690mOZrXbu3Dm9//772rJli0pLSysdZ9VJPjdv3lRwcLDjc9krGwUFBY5Nag3DUPv27bVz506X\n5wMAVIwudB660DwsPAEAgGrpqaeekiRduHBBY8aMkbe3twIDAys8NccwDK1fv97VESVJOTk5GjJk\niHJzcxUQECCbzabc3Fy1b99eZ8+eVV5engzDUGRkpLy9vS3J2LBhQ12/ft3x2d/fX5J09uxZtW3b\n1nE9Pz9fN2/edHk+AEDF6ELnoQvNUzWXQgEAAO7hwoULunDhgiTJbrerpKREWVlZjut//8cqs2fP\nVm5urkaPHq2tW7cqPj5ehmFo8eLF2rVrl2bNmqXQ0FDVqVNH8+bNsyRjkyZNdP78ecfn9u3by263\n66uvvnJcO3nypHbt2qWwsDArIgIAKkAXOg9daB6eeAIAANVSSkqK1RHuy9atWxUcHKxx48ZV+PX4\n+HglJSWpT58+SkpK0ujRo12c8OeNU6dOnaoTJ06oRYsW6t69u4KCgrR48WKlp6crJCREO3fuVHFx\nsfr27evyfACAitGFzkMXmoeFJwAAUC01atTI6gj3JTs7W127dnXsuVH2+kNxcbFj/4hmzZopOjpa\nq1atsmSy3adPH9lsNv3000+SpJo1a2rq1Kl6+eWXdfjwYR0+fFiS1LNnT7344osuzwcAqBhd6Dx0\noXlYeAIAuKWcnBzt3LlTly5dUmFhYYVjDMPQ2LFjXZwMnqZWrVry9fV1fK5Tp44kKTc3V0FBQY7r\nfn5+2rdvn8vzSVJoaKj+5V/+pdy1Tp06KSUlRampqcrLy1N4eHi5PS4AVH10IaoKutCzsfAEAHAr\ndrtd7733nhYtWiSbzea4djvDMGS325lswyUCAwOVnZ3t+Ny0aVNJ0v79+/Xss886rmdkZKhu3bou\nz3c3vr6+6t69u9UxADwguhBVDV3o2Vh4AgC4lTlz5mjBggXy8vJS9+7dFR4e7jgCF+7pu+++09q1\na3X69GkVFBTc8cuVZO1JPpGRkVq3bp2KiopUs2ZNdevWTXa7XZMnT1a9evUUHBysRYsW6fTp04qP\nj7ckIwD3Qhd6HroQVRkLTwAAt7J06VL5+Pjoiy++UOfOna2OAxPZbDa9+uqrSklJqXCCLZW/o2+V\nHj16KDk5WSkpKUpISFDz5s01YMAALVmyRH/4wx8k/fwkQo0aNfTaa69ZknHlypWaOnWq3nnnnUrv\n6m7evFmTJk3ShAkTlJCQ4OKEAB4EXeg56ELnoQvN42V1AAAAnOn8+fPq3LkzE20PsGjRIq1fv14R\nERH67LPP9A//8A8yDENr1qzRrFmz9Nxzz0mSRo8ebdkdXklKSEhQZmZmuQnqpEmTNH78eLVt21aN\nGjVSjx499MUXXygiIsKSjKtXr1ZBQYFiY2MrHdOlSxfl5+dr5cqVLkwG4JegCz0HXeg8dKF5eOIJ\nAOBWGjRooIYNG1odAy6wYsUK1apVS//7v/+rgIAAxySwWbNmatasmeLj49W1a1dNnDhRMTExVerk\nHx8fH40aNUqjRo2yOook6ciRI2rVqpVq1qxZ6ZiaNWuqdevWyszMdGEyAL8EXeg56ELnoQvNwxNP\nAAC30qVLFx06dMjqGHCBEydOqGPHjgoICCh3/fZXDQYMGKCWLVsqKSnJ1fGqlStXrigwMPCe4wID\nA3X16lUXJALwMOhCz0EXOg9daB4WngAAbmXcuHHKzc3VjBkzrI4CkxUVFZWbaNeqVUuSlJ+fX25c\nq1at9MMPP7g02+1ycnK0cuVKnT59utIxp06d0sqVK5WTk+O6YLepU6eOcnNz7znu2rVrd70TDKBq\noAs9B13oPHSheXjVDgDgVvbu3avExERNnz5dmzdvVvfu3RUaGiovr4rvtfTr18/FCeEsjz32WLk7\njmUT75MnT6pjx46O61euXFFxcbHL85WZN2+ePvvsM61atarSMTabTX/84x81evRovf766y5M97NW\nrVpp7969unLlyh13zctcvnxZaWlpevzxx12cDsCDogs9B13oPHSheVh4AgC4lTfeeMNxesuBAwd0\n8ODBu45nsl19NW/eXCdOnHB87tSpk+x2u+bMmaOPP/5YhmFoz549Sk1NtWyjUknaunWrWrZsqRYt\nWlQ6pkWLFvrVr36lzZs3WzLZ7t27t1JTU/Xqq6/qk08+kZ+fX7mvX79+Xa+99pqKioocG9UCqLro\nQs9BFzoPXWgeFp4AAG6lX79+lh4XDNfp3r27tm7dqoMHDyoyMlJdunRReHi4UlJS1L17dwUGBuro\n0aOy2+164YUXLMuZnZ2t6Ojoe45r0qSJ0tLSXJDoTgMGDFBycrL27t2rp59+Wr169VJ4eLikn199\nSElJUUFBgSIjIzV48GBLMgK4f3Sh56ALnYcuNA8LTwAAtzJ58mSrI8BF+vTpo0cffVT16tWTJHl7\ne+uTTz7RK6+8omPHjunKlSvy8vLS8OHDNWjQIMtyFhYW3tdeEDVr1tTNmzddkOhOPj4+mj17tt54\n4w1t2LBBK1ascPzSWrZB7a9//WtNnjxZNWrUsCQjgPtHF3oOutB56ELzGPbbt7sHAABwAydPnlRe\nXp6aNm0qf39/S7M888wz8vLy0tq1a+867tlnn1VxcbE2bNjgomQVy8zM1ObNm5WVlSXDMBQSEqLu\n3burTZs2luYCADwYuvCXowudiyeeAABu7cyZM8rNzZWfn5+aN29udRy4SNmj8VVBbGyslixZomXL\nllW6j8ry5ct15swZ9e/f38Xp7hQREWHpPiAAnI8u9Ex04S9HFzpXxccaAABQjZWUlGj69Onq2rWr\n/vEf/1HDhg3T7NmzHV9fsWKFhg4dqqNHj1qYEr9Edna2MjMzdeXKlXuOvXz5sjIzM3Xx4kUXJKvc\nyJEj5e3trYkTJ2ratGnKyspyfC07O1vTpk3TxIkT5ePjoxdffNG6oADcCl3ovuhCVDc88QQAcCsl\nJSUaNWqUduzYIW9vb7Vo0ULHjx8vNyYqKkr//u//rnXr1qlVq1YWJcWDunHjhhITE1VSUqKlS5fe\nc/ytW7f0m9/8Rr6+vvq///s/+fr6uiDlnVq0aKFJkybprbfe0syZMzVz5kzHPhdFRUWSJC8vL73z\nzjtq3bq1JRkBuBe60H3RhaiOeOIJAOBWFixYoO3btysuLk4bNmzQqlWr7hjTuHFjNW3aVFu3brUg\nIX6plStX6tq1axozZozCwsLuOT4sLEz/+q//qsuXL1f4/wNXGjBggBYuXKgePXqoZs2aKiwsdGy0\n2qNHDy1cuNDSTV8BuBe60H3RhaiOeOIJAOBWVqxYIT8/P02dOlUNGjSodFx4eLgyMjJcmAwP6/vv\nv1fNmjUf6DjooUOH6qOPPtL69es1cOBAE9PdW8eOHTVr1iyVlJTo6tV7Dp+lAAAYFElEQVSrMgxD\n/v7+8vFhOgbAuehC90UXojrivy4AwK2cOnVKMTExd51oS1LdunWVm5vrolRwhszMTLVv31516tS5\n7++pXbu2IiMjlZmZaWKyB+Pj46OgoCCrYwBwY3Sh+6ILUR3xqh0AwO0YhnHPMZcuXVKtWrVckAbO\nkpubq+Dg4Af+vqCgIH6xAuBx6EL3RBeiOmLhCQDgVho3bqwjR47IZrNVOubWrVs6cuSIWrRo4cJk\neFg+Pj4qLi5+4O8rLi6Wt7e3CYkAoGqiC90XXYjqiIUnAIBb6dWrly5evKjPPvus0jFz5szRjz/+\nqF69erkwGR7WY489ppMnTz7w9508eVIBAQEmJHIfb775pv7617/ec9zSpUv15ptvuiARgIdBF7ov\nutA8dKF5WHgCALiVkSNHKiAgQP/zP/+jf/u3f9O6deskSdeuXdOmTZv05ptvasaMGQoJCdGwYcMs\nTosH0aFDB504cULHjh277+85evSojh8/ro4dO5qYrPpLTk5WWlraPcft3btXy5Ytc0EiAA+DLnRf\ndKF56ELzsPAEAHArfn5+mjNnjkJDQ7V69WqNGzdOhmFo06ZNGjNmjJKTkxUcHKxPP/1U9erVszou\nHkDv3r1lt9v19ttvq6io6J7ji4uL9fbbb8swDPXu3dsFCd1fSUmJvLyYPgJVHV3ovuhC69GFD45T\n7QAAbqd169b69ttvtWTJEm3evFnnz59XaWmpQkJC1KNHDw0ePPiBToNB1RAfH6/o6Gjt2bNHI0aM\n0DvvvKOIiIgKx2ZmZuqdd97RgQMH9MQTTyg+Pt7Fad3TsWPHVL9+fatjALgPdKF7ogutRxc+OMNu\nt9utDgEAAHA/cnNzNXToUJ09e1aGYahVq1Zq3769GjZsKEm6evWqDh06pKNHj8putyssLEyLFi2y\ndF+LnJwc1alT556T1Pz8fN28edNlx0vfvj9FcnKymjZtqqioqArHlpaW6sSJE0pPT1d8fLw+/fRT\nl2QEANyJLnQeutA1WHgCAADVSn5+viZNmqRvv/3WcWLT7ceG2+12eXl5KSEhQW+99ZYeeeQRq6JK\nktq0aaP+/fvr/fffv+u4iRMnaunSpUpPT3dJrtvvkBuGofuZEgYEBCgpKUmtW7c2MxoA4B7oQueg\nC12DV+0AAEC1Ur9+fU2ZMkXjxo3T999/rx9++EG5ubmSJH9/fz3++OPq2bOnmjRpYnHSn9nt9vua\nyJaNdZUPPvjA8TP/9Kc/6YknntDAgQMrHFujRg0FBQWpQ4cOqlmzpssyAgAqRhc6B13oGiw8AQCq\ntREjRvzi7zUMQ3PnznViGrhSWFjYQ/33r2ry8/NdOpHt37+/4+/Tp09Xhw4dyl0DUH3QhZ6LLnw4\ndKFrsPAEAKjWdu/efd+PRv+92x9JB5wpJyen3Odbt27dca1M2Z4R27ZtU6NGjVwR7w4bNmyw5OcC\ncA66EFURXYgyLDwBANxCZGSk+vbta+nGmUCZ+Pj4cr/MrVmzRmvWrLnr99jtdv3ud78zO9p9uXTp\nkuOXg6CgIAUGBlqcCMD9oAtRldCFKMPCEwCgWuvdu7fWr1+vgwcPKj09Xd26dVNiYqJ69eolHx9q\nDtYIDAx0TLYvXbokX19fNWjQoMKxZXtGPPPMM5a/LvHNN98oKSlJZ8+eLXe9adOm+v3vf69BgwZZ\nlAzA3dCFqIroQpThVDsAQLVXUFCg1atXKzk5Wfv375dhGHrkkUfUp08fJSYmqk2bNlZHhAeLiIhQ\n//79HRuYVlVvvPGGli9fLrvdLsMwHHd2L1265LjWr1+/Kv+/A/BUdCGqMrrQs7HwBABwK6dOndLS\npUu1fPlyXbp0SYZhqHXr1kpMTFTv3r3l7+9vdUR4mMWLF6t58+bq3Lmz1VEqtWrVKk2YMEENGzbU\nK6+8osTERMfmrkVFRVq6dKmmT5+uq1evasqUKXruuecsTgzgbuhCVDV0oWdj4QkA4JZsNpu2bNmi\n5ORkbdiwQcXFxfL29lZCQoL+8pe/WB0PqFJGjBihffv2KTk5WS1btqxwzPHjx9WvXz9FRUVp3rx5\nLk4I4JegC4H7Rxeax8vqAAAAmMHLy0vx8fGaOnWqNm3apPj4eJWUlGjr1q1WRwOqnMzMTMXExFQ6\n0Zakli1bKjY2VpmZmS5MBuBh0IXA/aMLzcNOcwAAt3Xy5EklJydr+fLlunz5siQpPDzc4lRwd88+\n+6wkKSkpSY0bN3Z8vl9r1641I9Zd/fTTT/Lz87vnOD8/P926dcsFiQA4C10IK9CFuB0LTwAAt1JQ\nUKBVq1YpOTlZBw8elN1ul5+fn4YPH67ExES1bdvW6ohwc2fOnJFhGCouLnZ8vl+3HzvtSkFBQY5/\nL5VlsNvtOnToEMdJA9UAXQir0YW4HQtPAIBqz263a9u2bUpOTlZKSopu3bolb29vxcfHq3///urV\nq5dq1KhhdUx4iHXr1kmSGjVqVO5zVdatWzd9/fXX+vOf/6wJEybI29u73NdtNpumTJmic+fOaejQ\noRalBHA3dCGqEroQt2NzcQBAtfbhhx86Tu2x2+1q0aKFEhMT1bdvXwUEBFgdD6gWsrKy1K9fP+Xn\n56tRo0bq3bu3GjduLMMwdO7cOa1evVrnz59XgwYNtGzZMoWEhFgdGcBt6ELg4dGF5mHhCQBQrUVE\nRMgwDLVr1079+/dXhw4dHuj7H3/8cZOSAdXL/v379dprr+nixYt3vGJgt9sVEhKiqVOnPvC/MQDm\nowsB56ALzcHCEwCgWiubbP8ShmEoPT3dyYmA6quoqEjfffedUlNTlZOTI+nnPS+io6OVkJCgmjVr\nWpwQQEXoQsB56ELnY+EJAFCt9erV66G+f8OGDU5KAgCANehCAFUZC08AAAAAAAAwBafaAQAAwKG0\ntFTXr19XYWFhpWNCQ0NdmAgAANeiC52LhScAAADowIEDmjZtmvbs2aOioqJKx7EfDADAXdGF5mDh\nCQAAwMOlpaVp5MiRjkn2I488orp161qcCgAA16ELzcPCEwAAgIf7+OOPVVRUpMGDB2vcuHFq2LCh\n1ZEAAHAputA8bC4OAABgAbvdruTkZGVmZio0NFSDBg2y7M5qVFSUQkJCtHr1akt+PgDAM9GFnoEn\nngAAAEw0Z84cffLJJ5o5c6ZiY2Md18eMGaPNmzfLbrfLMAwtWbJE33zzjWrXru3yjHa7Xa1bt3b5\nzwUAeAa60LN5WR0AAADAnW3evFm+vr6Kjo52XNu2bZs2bdqkxx57TKNGjVK7du10/PhxLVmyxJKM\nrVu31uXLly352QAA90cXejYWngAAAEx05swZtWjRQl5ef5t2rV27VoZh6MMPP9T48eM1b948NWjQ\nQKtWrbIk44gRI5SWlqaMjAxLfj4AwL3RhZ6NhScAAAATXb9+XYGBgeWupaWlqWHDhurcubMkqXbt\n2urUqZPOnz9vRUT90z/9k8aMGaORI0fqyy+/VFZWliU5AADuiS70bOzxBAAAYLJbt245/p6fn69T\np07p6aefLjemfv36+vHHH10dTZLUpk0bx9/fffddvfvuu5WONQxD6enprogFAHAjdKHnYuEJAADA\nRI0bN9aBAwdks9nk5eWljRs3ymaz6Yknnig37tq1a3r00UctyfgghxxzIDIA4EHRhZ6NhScAAAAT\n/frXv9acOXP06quvKi4uTrNmzZK3t7d69epVblx6erqaNWtmScbMzExLfi4AwDPQhZ6NhScAAAAT\njRo1SuvXr3f8kaSRI0cqLCzMMSYtLU25ubkaMGCAVTEBADANXejZDDvPiAEAAJjq5s2b+u6773T1\n6lW1b99ecXFx5b6+bt067dq1SwMHDiy3xwQAAO6CLvRcLDwBAAAAAADAFF5WBwAAAAAAAIB7Yo8n\nAAAAFyksLNTZs2dVUFBQ6Yk4UVFRLk4FAIDr0IWeh4UnAAAAk507d07vv/++tmzZotLS0krHGYah\n9PR0FyYDAMA16ELPxcITAACAiXJycjRkyBDl5uYqICBANptNubm5at++vc6ePau8vDwZhqHIyEh5\ne3tbHRcAAKejCz0bezwBAACYaPbs2crNzdXo0aO1detWxcfHyzAMLV68WLt27dKsWbMUGhqqOnXq\naN68eVbHBQDA6ehCz8bCEwAAgIm2bt2q4OBgjRs3rsKvx8fHKykpSXv27FFSUpKL0wEAYD660LOx\n8AQAAGCi7OxsRUREyMvr52mXYRiSpOLiYseYZs2aKTo6WqtWrbIk47Vr13Tw4EHl5uaWu56Tk6MJ\nEyaoT58+GjNmDHtuAAB+EbrQs7HwBAAAYKJatWrJ19fX8blOnTqSdMfE1s/PT+fPn3dptjKzZ8/W\nkCFDdOnSJce1oqIivfDCC1q9erWOHTumjRs36re//a2ys7MtyQgAqL7oQs/GwhMAAICJAgMDy01Q\nmzZtKknav39/uXEZGRmqW7euS7OV2bVrl8LCwhQREeG4tnr1amVlZSk2Nlaff/65fvOb3yg/P18L\nFiywJCMAoPqiCz0bC08AAAAmioyM1PHjx1VUVCRJ6tatm+x2uyZPnqxt27bpxIkT+q//+i+dPn1a\n7dq1syRjTk6OwsLCyl3buHGjDMPQu+++q7i4OP3Hf/yHwsLCtGXLFksyAgCqL7rQs7HwBAAAYKIe\nPXroxo0bSklJkSQ1b95cAwYMUHZ2tv7whz+od+/eWrBggXx8fPTaa69ZkjEvL0+PPvpouWv79+9X\n8+bNy03C27Rpo4sXL7o6HgCgmqMLPZuP1QEAAADcWUJCghISEspdmzRpkpo2baq1a9cqLy9P4eHh\nGj16dLnH+13J19dX165dc3zOyspSTk6OBg4cWG5cjRo1ym0ECwDA/aALPRsLTwAAAC7m4+OjUaNG\nadSoUVZHkSS1bNlSe/fuVW5urvz9/bVy5UoZhqHOnTuXG3fx4kU1bNjQopQAAHdCF3oOXrUDAADw\ncH379tVPP/2kgQMH6uWXX9bHH3+sunXr6umnn3aMKSwsVHp6usLDwy1MCgCAOehC8/DEEwAAgBN1\n795dsbGxio2NVUxMjOPknqpsyJAhOnDggJYtW6asrCzVrVtX7733nurVq+cYk5KSop9++knR0dEW\nJgUAVAd0IW5n2O12u9UhAAAA3EVERIQMw3B8DgwMVExMjGMC/vcn5lQlWVlZunr1qsLDw+84zjoj\nI0MXLlxQx44dFRAQYFFCAEB1QBfidiw8AQAAONHGjRu1a9cupaamKiMjQ6WlpZLkmIAHBwc7Jt8x\nMTFq3LixlXEBAHA6uhC3Y+EJAADAJAUFBUpLS9Pu3buVmpqq9PR0lZSUSPrb5DskJKTc6wihoaFW\nRgYAwKnoQrDwBAAA4CI3btxQWlqaUlNTtWvXLsfku2zibRiG0tPTLctXWFionTt36vTp0yooKFBF\n00TDMDR27FgL0gEA3AFd6HlYeAIAALBIQUGB5s2bp7lz5yovL0+GYSgjI8OSLGvXrtXbb7+tvLy8\nSsfY7XZLMwIA3A9d6P441Q4AAMBF7Ha7Dh8+rN27d2v37t1KS0vTjRs3HHdTrTqe+cCBAxo/frwM\nw9Bzzz2nY8eO6ejRoxo1apTOnDmj7du3Kz8/XwMHDlRwcLAlGQEA7oEu9DwsPAEAAJjEZrM5Jtep\nqanlJteGYahVq1aKjo52/PH397ckZ1JSkmw2m2bOnKmePXvqzTff1NGjR/X6669LknJzc/WnP/1J\nmzZtUnJysiUZAQDVE10IFp4AAACc6ODBg467uHv37nVMrr29vdWmTRvHxLpz585q0KCB1XElSfv2\n7dOvfvUr9ezZs8Kv+/v7a8qUKXrqqac0bdo0/ed//qdrAwIAqhW6ELdj4QkAAMCJBg8eLMMw5OPj\no3bt2jkm11FRUapbt67V8Sp07do1RUVFOT57e3tLkm7duiVfX19JUr169RQdHa0tW7ZYkhEAUH3Q\nhbidl9UBAAAA3FGzZs3UqVMnRUVFqVOnTlV2oi1JjzzyiIqKihyf69evL0m6ePFiuXGGYejq1asu\nzQYAqL7oQkg88QQAAOBU48ePd7xa8Nlnn+nzzz+Xl5eXWrdurc6dOysmJkadO3eWn5+f1VEdgoOD\nlZ2d7fjcqlUr2e12bdy4US+++KIk6ebNm0pLS1NQUJBFKQEA1QVdiNsZ9rKt4wEAAOA0paWl+uGH\nH7R7927t2rVL+/btU0FBgQzDkGEYatGihePVg5iYGDVs2NCyrP/93/+t+fPna/PmzfL399e1a9fU\nq1cvlZSUaMSIEQoODtayZcuUnp6uwYMHa9KkSZZlBQBUH3QhJBaeAAAAXMJmszkm32V3gfPz8yX9\n/Nh+s2bNFB0dbclmpQcPHtRHH32k3//+9+rWrZsk6auvvio3qbbb7QoJCdGSJUssO3EIAFC90YWe\niYUnAAAAC9hsNmVkZOj777/X/PnzlZeXJ8MwlJGRYXU0h0OHDmndunXKy8tTeHi4EhMTq8zpQwCA\n6o8u9AwsPAEAALjQzZs3tWfPHsfd3vT0dJWWlsput1e5yTYAAGagCz0Lm4sDAACY6MaNG9qzZ49S\nU1PLTa6lnx/Zl6TQ0FDFxMQoNjbWyqgAAJiCLvRsLDwBAAA4UdnkuuwubkZGRoWT6+joaMXGxiom\nJkaNGze2MrJiY2MVGxurLl26KC4uTs2bN7c0DwCgeqMLcTtetQMAAHCidu3a3TG5DgkJKTe5DgsL\nszLiHR5//HGVlpbKMAxJUlBQkOLi4tSlSxd17dpVjz32mMUJAQDVCV2I27HwBAAA4EQREREKDg52\nTK5jY2Or3OT67xUUFCg1NVU7duzQjh07dOzYMUlyTL7Dw8MVFxenuLg4xcbGql69elbGBQBUcXQh\nbsfCEwAAgBOdPXtWTZo0sTrGQ7l69apj4r1z505duHDBMfH29vbW4cOHLU4IAKjK6ELcjoUnAAAA\nVOrMmTP6+uuvtXDhQhUWFnLaEADA49CFD4fNxQEAAOCQm5urnTt3avv27dqxY4eysrIcX2vbtq3i\n4uIsTAcAgPnoQufiiScAAAAPt2XLFsfk+ujRo7LZbJKkJk2aOPaz6NKli/z8/CxOCgCAOehC87Dw\nBAAA4OEiIiJkGIYaNmyo2NhYxcXFqWvXrgoNDbU6GgAALkEXmsfL6gAAAACwXtm9SMMw5OXlJS8v\npokAAM9CF5qDJ54AAAA83IYNGxyn9hw7dsxxak+TJk3UtWtXx+sFDRo0sDgpAADmoAvNw8ITAAAA\nHK5cuVLu+OisrCzHnd+IiAg9+eSTGj9+vNUxAQAwDV3oXCw8AQAAoFLnzp3Tl19+qS+//JIjpAEA\nHokufDg+VgcAAABA1XL16lXHnd4dO3YoOzvbse8F+10AADwBXeg8LDwBAAB4uJs3b2r37t3asWOH\ntm/fruPHj0v62yarzZs3d+xvERsba2VUAABMQReah1ftAAAAPFy7du1UWlrqmFwHBgYqLi7O8Sco\nKMjihAAAmIsuNA9PPAEAAHi42rVrKzY21jG5Dg8PtzoSAAAuRReahyeeAAAAPJzNZmO/CgCAR6ML\nzcPCEwAAAAAAAEzBch4AAAAAAABMwcITAAAAAAAATMHCEwAAAAAAAEzBwhMAAAAAAABMwcITAAAA\nAAAATMHCEwAAAAAAAEzBwhMAAICHmz59ulJSUu45bsOGDZo+fboLEgEA4Fp0oXlYeAIAAPBw06dP\n1/r16+85bsOGDZoxY4YLEgEA4Fp0oXlYeAIAAMB9KS0tlWEYVscAAMAydOGDY+EJAAAA9+XcuXOq\nV6+e1TEAALAMXfjgfKwOAAAAANf7+/0pMjMzK92zorS0VCdOnFBaWppiYmJcEQ8AANPRha5h2O12\nu9UhAAAA4FoREREyDEMPMhWsXbu2Zs2axYQbAOAW6ELX4IknAAAADzR27FjHZHvGjBlq06aNnnrq\nqQrH1qhRQ0FBQerWrZsCAgJcnBQAAHPQha7BE08AAAAeLiIiQv3799cHH3xgdRQAACxBF5qHhScA\nAAAAAACYglPtAAAAAAAAYAr2eAIAAPAwqampD/X90dHRTkoCAIA16ELX4VU7AAAAD1N2is8vYRiG\n0tPTnZwIAADXogtdhyeeAAAAPExoaKjVEQAAsBRd6Do88QQAAAAAAABTsLk4AAAAAAAATMHCEwAA\nAAAAAEzBwhMAAAAAAABMwcITAAAAAAAATMHCEwAAAAAAAEzBwhMAAAAAAABMwcITAAAAAAAATPH/\nD/QvAlhtGFoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqsgRqrNnSHy",
        "colab_type": "code",
        "outputId": "72015192-fdf0-40a2-a226-60138abf035b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        }
      },
      "source": [
        "sents = get_sents(dev_set)\n",
        "run_and_plot(sents, base_enc_model, enc_model)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V2fbH41hy2ntRehC50JbyoO3/5gAAAAAAAAD5YOF5AAAA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\nWrV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            "text/plain": [
              "<Figure size 1152x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ct_ayUUKGkQO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BVldmAiGxoAm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}